このレポートでは、Weekly_HSP_Projectの分析経過を報告します。分析の構成は以下のとおりです。分析の再現性を担保するために用いたコードも記しています。
#tidyverseパッケージ読み込み
library(tidyverse)
## -- Attaching packages ----------------------------------------- tidyverse 1.2.1 --
## √ ggplot2 2.2.1 √ purrr 0.2.5
## √ tibble 1.4.2 √ dplyr 0.7.6
## √ tidyr 0.8.1 √ stringr 1.3.0
## √ readr 1.1.1 √ forcats 0.3.0
## -- Conflicts -------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
#データ読み込み
lowdata <- read_csv("lowdata_4timepoints.csv", na = c(".", ""))
## Parsed with column specification:
## cols(
## .default = col_integer()
## )
## See spec(...) for full column specifications.
lowdata$gender_T1 <- factor(lowdata$gender_T1) #性別をfactor型に変換
lowdata$gender_T2 <- factor(lowdata$gender_T2) #性別をfactor型に変換
lowdata$gender_T3 <- factor(lowdata$gender_T3) #性別をfactor型に変換
lowdata$gender_T4 <- factor(lowdata$gender_T4) #性別をfactor型に変換
head(lowdata) #先頭6行確認
names(lowdata) #変数名確認
## [1] "ID" "school" "grade" "age_T1" "gender_T1"
## [6] "hsc1_T1" "hsc2_T1" "hsc3_T1" "hsc4_T1" "hsc5_T1"
## [11] "hsc6_T1" "hsc7_T1" "hsc8_T1" "hsc9_T1" "hsc10_T1"
## [16] "hsc11_T1" "hsc12_T1" "wb1_T1" "wb2_T1" "wb3_T1"
## [21] "wb4_T1" "wb5_T1" "ev1_T1" "ev2_T1" "age_T2"
## [26] "gender_T2" "hsc1_T2" "hsc2_T2" "hsc3_T2" "hsc4_T2"
## [31] "hsc5_T2" "hsc6_T2" "hsc7_T2" "hsc8_T2" "hsc9_T2"
## [36] "hsc10_T2" "hsc11_T2" "hsc12_T2" "wb1_T2" "wb2_T2"
## [41] "wb3_T2" "wb4_T2" "wb5_T2" "ev1_T2" "ev2_T2"
## [46] "age_T3" "gender_T3" "hsc1_T3" "hsc2_T3" "hsc3_T3"
## [51] "hsc4_T3" "hsc5_T3" "hsc6_T3" "hsc7_T3" "hsc8_T3"
## [56] "hsc9_T3" "hsc10_T3" "hsc11_T3" "hsc12_T3" "wb1_T3"
## [61] "wb2_T3" "wb3_T3" "wb4_T3" "wb5_T3" "ev1_T3"
## [66] "ev2_T3" "age_T4" "gender_T4" "hsc1_T4" "hsc2_T4"
## [71] "hsc3_T4" "hsc4_T4" "hsc5_T4" "hsc6_T4" "hsc7_T4"
## [76] "hsc8_T4" "hsc9_T4" "hsc10_T4" "hsc11_T4" "hsc12_T4"
## [81] "wb1_T4" "wb2_T4" "wb3_T4" "wb4_T4" "wb5_T4"
## [86] "ev1_T4" "ev2_T4"
data <- lowdata %>%
dplyr::mutate(eoe_T1 = (hsc4_T1 + hsc6_T1 + hsc8_T1 + hsc9_T1 + hsc12_T1)/5, na.rm = TRUE) %>% #EOE_T1の平均
dplyr::mutate(lst_T1 = (hsc2_T1 + hsc11_T1)/2, na.rm = TRUE) %>% #LST_T1の平均
dplyr::mutate(aes_T1 = (hsc5_T1 + hsc10_T1 + hsc1_T1 + hsc3_T1)/4, na.rm = TRUE) %>% #AES_T1の平均
dplyr::mutate(hsc_T1 = (eoe_T1 + lst_T1 + aes_T1)/3, na.rm = TRUE) %>% #HSC_T1の平均
dplyr::mutate(eoe_T2 = (hsc4_T2 + hsc6_T2 + hsc8_T2 + hsc9_T2 + hsc12_T2)/5, na.rm = TRUE) %>% #EOE_T2の平均
dplyr::mutate(lst_T2 = (hsc2_T2 + hsc11_T2)/2, na.rm = TRUE) %>% #LST_T2の平均
dplyr::mutate(aes_T2 = (hsc5_T2 + hsc10_T2 + hsc1_T2 + hsc3_T2)/4, na.rm = TRUE) %>% #AES_T2の平均
dplyr::mutate(hsc_T2 = (eoe_T2 + lst_T2 + aes_T2)/3, na.rm = TRUE) %>% #HSC_T2の平均
dplyr::mutate(eoe_T3 = (hsc4_T3 + hsc6_T3 + hsc8_T3 + hsc9_T3 + hsc12_T3)/5, na.rm = TRUE) %>% #EOE_T3の平均
dplyr::mutate(lst_T3 = (hsc2_T3 + hsc11_T3)/2, na.rm = TRUE) %>% #LST_T3の平均
dplyr::mutate(aes_T3 = (hsc5_T3 + hsc10_T3 + hsc1_T3 + hsc3_T3)/4, na.rm = TRUE) %>% #AES_T3の平均
dplyr::mutate(hsc_T3 = (eoe_T3 + lst_T3 + aes_T3)/3, na.rm = TRUE) %>% #HSC_T3の平均
dplyr::mutate(eoe_T4 = (hsc4_T4 + hsc6_T4 + hsc8_T4 + hsc9_T4 + hsc12_T4)/5, na.rm = TRUE) %>% #EOE_T4の平均
dplyr::mutate(lst_T4 = (hsc2_T4 + hsc11_T4)/2, na.rm = TRUE) %>% #LST_T4の平均
dplyr::mutate(aes_T4 = (hsc5_T4 + hsc10_T4 + hsc1_T4 + hsc3_T4)/4, na.rm = TRUE) %>% #AES_T4の平均
dplyr::mutate(hsc_T4 = (eoe_T4 + lst_T4 + aes_T4)/3, na.rm = TRUE) %>% #HSC_T4の平均
dplyr::mutate(hsc_onemonth = (hsc_T1 + hsc_T2 + hsc_T3 + hsc_T4)/4, na.rm = TRUE) %>% #1ヵ月間のHSC平均
dplyr::mutate(wb_T1 = (wb1_T1 + wb2_T1 + wb3_T1 + wb4_T1 + wb5_T1)/5, na.rm = TRUE) %>% #wb_T1の平均
dplyr::mutate(wb_T2 = (wb1_T2 + wb2_T2 + wb3_T2 + wb4_T2 + wb5_T2)/5, na.rm = TRUE) %>% #wb_T2の平均
dplyr::mutate(wb_T3 = (wb1_T3 + wb2_T3 + wb3_T3 + wb4_T3 + wb5_T3)/5, na.rm = TRUE) %>% #wb_T3の平均
dplyr::mutate(wb_T4 = (wb1_T4 + wb2_T4 + wb3_T4 + wb4_T4 + wb5_T4)/5, na.rm = TRUE) %>% #wb_T4の平均
dplyr::mutate(wb_onemonth = (wb_T1 + wb_T2 + wb_T3 + wb_T4)/4, na.rm = TRUE) %>% #1か月間のwb平均
dplyr::mutate(ev_T1 = (as.numeric(ev1_T1) + as.numeric(ev2_T1))/2, na.rm = TRUE) %>% #event_T1の平均 #as.numericにしないとエラーが出る
dplyr::mutate(ev_T2 = (as.numeric(ev1_T2) + as.numeric(ev2_T2))/2, na.rm = TRUE) %>% #event_T2の平均
dplyr::mutate(ev_T3 = (as.numeric(ev1_T3) + as.numeric(ev2_T3))/2, na.rm = TRUE) %>% #event_T3の平均
dplyr::mutate(ev_T4 = (as.numeric(ev1_T4) + as.numeric(ev2_T4))/2, na.rm = TRUE) %>% #event_T4の平均
dplyr::mutate(ev_onemonth = (ev_T1 + ev_T2 + ev_T3 + ev_T4)/4, na.rm = TRUE) %>% #1か月間のev平均
dplyr::select(-na.rm) #謎にna.rmという変数が勝手に作成されてしまうのでそれを除外
head(data) #先頭6行確認
names(data) #変数名確認
## [1] "ID" "school" "grade" "age_T1"
## [5] "gender_T1" "hsc1_T1" "hsc2_T1" "hsc3_T1"
## [9] "hsc4_T1" "hsc5_T1" "hsc6_T1" "hsc7_T1"
## [13] "hsc8_T1" "hsc9_T1" "hsc10_T1" "hsc11_T1"
## [17] "hsc12_T1" "wb1_T1" "wb2_T1" "wb3_T1"
## [21] "wb4_T1" "wb5_T1" "ev1_T1" "ev2_T1"
## [25] "age_T2" "gender_T2" "hsc1_T2" "hsc2_T2"
## [29] "hsc3_T2" "hsc4_T2" "hsc5_T2" "hsc6_T2"
## [33] "hsc7_T2" "hsc8_T2" "hsc9_T2" "hsc10_T2"
## [37] "hsc11_T2" "hsc12_T2" "wb1_T2" "wb2_T2"
## [41] "wb3_T2" "wb4_T2" "wb5_T2" "ev1_T2"
## [45] "ev2_T2" "age_T3" "gender_T3" "hsc1_T3"
## [49] "hsc2_T3" "hsc3_T3" "hsc4_T3" "hsc5_T3"
## [53] "hsc6_T3" "hsc7_T3" "hsc8_T3" "hsc9_T3"
## [57] "hsc10_T3" "hsc11_T3" "hsc12_T3" "wb1_T3"
## [61] "wb2_T3" "wb3_T3" "wb4_T3" "wb5_T3"
## [65] "ev1_T3" "ev2_T3" "age_T4" "gender_T4"
## [69] "hsc1_T4" "hsc2_T4" "hsc3_T4" "hsc4_T4"
## [73] "hsc5_T4" "hsc6_T4" "hsc7_T4" "hsc8_T4"
## [77] "hsc9_T4" "hsc10_T4" "hsc11_T4" "hsc12_T4"
## [81] "wb1_T4" "wb2_T4" "wb3_T4" "wb4_T4"
## [85] "wb5_T4" "ev1_T4" "ev2_T4" "eoe_T1"
## [89] "lst_T1" "aes_T1" "hsc_T1" "eoe_T2"
## [93] "lst_T2" "aes_T2" "hsc_T2" "eoe_T3"
## [97] "lst_T3" "aes_T3" "hsc_T3" "eoe_T4"
## [101] "lst_T4" "aes_T4" "hsc_T4" "hsc_onemonth"
## [105] "wb_T1" "wb_T2" "wb_T3" "wb_T4"
## [109] "wb_onemonth" "ev_T1" "ev_T2" "ev_T3"
## [113] "ev_T4" "ev_onemonth"
# write.csv(data, file = "data_for_analysis.csv", na = ".") #csvで書き出し
library(plotly)
#性別T1の度数分布とヒストグラム
gender_T1_count <- dplyr::count(data, gender_T1)
knitr::kable(gender_T1_count) #テーブル化
| gender_T1 | n |
|---|---|
| 0 | 43 |
| 1 | 71 |
a <- ggplot(data = data, mapping = aes(x = gender_T1, fill = factor(gender_T1))) + geom_bar() #視覚化
ggplotly(a) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T2の度数分布とヒストグラム
gender_T2_count <- dplyr::count(data, gender_T2)
knitr::kable(gender_T2_count) #テーブル化
| gender_T2 | n |
|---|---|
| 0 | 38 |
| 1 | 62 |
| NA | 14 |
b <- ggplot(data = data, mapping = aes(x = gender_T2, fill = factor(gender_T2))) + geom_bar() #視覚化
ggplotly(b) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T3の度数分布とヒストグラム
gender_T3_count <- dplyr::count(data, gender_T3)
knitr::kable(gender_T3_count) #テーブル化
| gender_T3 | n |
|---|---|
| 0 | 38 |
| 1 | 67 |
| NA | 9 |
c <- ggplot(data = data, mapping = aes(x = gender_T3, fill = factor(gender_T3))) + geom_bar() #視覚化
ggplotly(c) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T4の度数分布とヒストグラム
gender_T4_count <- dplyr::count(data, gender_T4)
knitr::kable(gender_T4_count) #テーブル化
| gender_T4 | n |
|---|---|
| 0 | 39 |
| 1 | 67 |
| NA | 8 |
d <- ggplot(data = data, mapping = aes(x = gender_T4, fill = factor(gender_T4))) + geom_bar() #視覚化
ggplotly(d) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T1の度数分布とヒストグラム
hsc1_T1_count <- dplyr::count(data, hsc1_T1)
knitr::kable(hsc1_T1_count) #テーブル化
| hsc1_T1 | n |
|---|---|
| 1 | 2 |
| 2 | 8 |
| 3 | 10 |
| 4 | 16 |
| 5 | 48 |
| 6 | 19 |
| 7 | 11 |
e <- ggplot(data = data, mapping = aes(x = hsc1_T1, fill = factor(hsc1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(e) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T1の度数分布とヒストグラム
hsc2_T1_count <- dplyr::count(data, hsc2_T1)
knitr::kable(hsc2_T1_count) #テーブル化
| hsc2_T1 | n |
|---|---|
| 1 | 3 |
| 2 | 12 |
| 3 | 10 |
| 4 | 20 |
| 5 | 29 |
| 6 | 24 |
| 7 | 16 |
f <- ggplot(data = data, mapping = aes(x = hsc2_T1, fill = factor(hsc2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(f) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T1の度数分布とヒストグラム
hsc3_T1_count <- dplyr::count(data, hsc3_T1)
knitr::kable(hsc3_T1_count) #テーブル化
| hsc3_T1 | n |
|---|---|
| 2 | 4 |
| 3 | 4 |
| 4 | 12 |
| 5 | 23 |
| 6 | 35 |
| 7 | 35 |
| NA | 1 |
g <- ggplot(data = data, mapping = aes(x = hsc3_T1, fill = factor(hsc3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(g) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T1の度数分布とヒストグラム
hsc4_T1_count <- dplyr::count(data, hsc4_T1)
knitr::kable(hsc4_T1_count) #テーブル化
| hsc4_T1 | n |
|---|---|
| 1 | 8 |
| 2 | 13 |
| 3 | 13 |
| 4 | 19 |
| 5 | 28 |
| 6 | 17 |
| 7 | 16 |
h <- ggplot(data = data, mapping = aes(x = hsc4_T1, fill = factor(hsc4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(h) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T1の度数分布とヒストグラム
hsc5_T1_count <- dplyr::count(data, hsc5_T1)
knitr::kable(hsc5_T1_count) #テーブル化
| hsc5_T1 | n |
|---|---|
| 3 | 1 |
| 4 | 4 |
| 5 | 21 |
| 6 | 36 |
| 7 | 52 |
i <- ggplot(data = data, mapping = aes(x = hsc5_T1, fill = factor(hsc5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(i) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T1の度数分布とヒストグラム
hsc6_T1_count <- dplyr::count(data, hsc6_T1)
knitr::kable(hsc6_T1_count) #テーブル化
| hsc6_T1 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 12 |
| 4 | 21 |
| 5 | 35 |
| 6 | 25 |
| 7 | 14 |
j <- ggplot(data = data, mapping = aes(x = hsc6_T1, fill = factor(hsc6_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(j) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T1の度数分布とヒストグラム
hsc7_T1_count <- dplyr::count(data, hsc7_T1)
knitr::kable(hsc7_T1_count) #テーブル化
| hsc7_T1 | n |
|---|---|
| 1 | 4 |
| 2 | 11 |
| 3 | 13 |
| 4 | 21 |
| 5 | 17 |
| 6 | 26 |
| 7 | 22 |
k <- ggplot(data = data, mapping = aes(x = hsc7_T1, fill = factor(hsc7_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(k) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T1の度数分布とヒストグラム
hsc8_T1_count <- dplyr::count(data, hsc8_T1)
knitr::kable(hsc8_T1_count) #テーブル化
| hsc8_T1 | n |
|---|---|
| 1 | 3 |
| 2 | 16 |
| 3 | 12 |
| 4 | 42 |
| 5 | 24 |
| 6 | 13 |
| 7 | 4 |
l <- ggplot(data = data, mapping = aes(x = hsc8_T1, fill = factor(hsc8_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(l) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T1の度数分布とヒストグラム
hsc9_T1_count <- dplyr::count(data, hsc9_T1)
knitr::kable(hsc9_T1_count) #テーブル化
| hsc9_T1 | n |
|---|---|
| 1 | 9 |
| 2 | 21 |
| 3 | 18 |
| 4 | 38 |
| 5 | 17 |
| 6 | 10 |
| 7 | 1 |
m <- ggplot(data = data, mapping = aes(x = hsc9_T1, fill = factor(hsc9_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(m) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T1の度数分布とヒストグラム
hsc10_T1_count <- dplyr::count(data, hsc10_T1)
knitr::kable(hsc10_T1_count) #テーブル化
| hsc10_T1 | n |
|---|---|
| 4 | 3 |
| 5 | 10 |
| 6 | 29 |
| 7 | 72 |
n <- ggplot(data = data, mapping = aes(x = hsc10_T1, fill = factor(hsc10_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(n) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T1の度数分布とヒストグラム
hsc11_T1_count <- dplyr::count(data, hsc11_T1)
knitr::kable(hsc11_T1_count) #テーブル化
| hsc11_T1 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 12 |
| 4 | 25 |
| 5 | 26 |
| 6 | 23 |
| 7 | 21 |
o <- ggplot(data = data, mapping = aes(x = hsc11_T1, fill = factor(hsc11_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(o) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T1の度数分布とヒストグラム
hsc12_T1_count <- dplyr::count(data, hsc12_T1)
knitr::kable(hsc12_T1_count) #テーブル化
| hsc12_T1 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 5 |
| 4 | 2 |
| 5 | 28 |
| 6 | 37 |
| 7 | 35 |
p <- ggplot(data = data, mapping = aes(x = hsc12_T1, fill = factor(hsc12_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(p) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T2の度数分布とヒストグラム
hsc1_T2_count <- dplyr::count(data, hsc1_T2)
knitr::kable(hsc1_T2_count) #テーブル化
| hsc1_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 4 |
| 3 | 10 |
| 4 | 20 |
| 5 | 37 |
| 6 | 22 |
| 7 | 5 |
| NA | 14 |
q <- ggplot(data = data, mapping = aes(x = hsc1_T2, fill = factor(hsc1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(q) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T2の度数分布とヒストグラム
hsc2_T2_count <- dplyr::count(data, hsc2_T2)
knitr::kable(hsc2_T2_count) #テーブル化
| hsc2_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 8 |
| 3 | 12 |
| 4 | 13 |
| 5 | 30 |
| 6 | 24 |
| 7 | 12 |
| NA | 14 |
r <- ggplot(data = data, mapping = aes(x = hsc2_T2, fill = factor(hsc2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(r) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T2の度数分布とヒストグラム
hsc3_T2_count <- dplyr::count(data, hsc3_T2)
knitr::kable(hsc3_T2_count) #テーブル化
| hsc3_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 11 |
| 5 | 22 |
| 6 | 33 |
| 7 | 28 |
| NA | 14 |
s <- ggplot(data = data, mapping = aes(x = hsc3_T2, fill = factor(hsc3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(s) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T2の度数分布とヒストグラム
hsc4_T2_count <- dplyr::count(data, hsc4_T2)
knitr::kable(hsc4_T2_count) #テーブル化
| hsc4_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 11 |
| 3 | 10 |
| 4 | 11 |
| 5 | 27 |
| 6 | 25 |
| 7 | 9 |
| NA | 14 |
t <- ggplot(data = data, mapping = aes(x = hsc4_T2, fill = factor(hsc4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(t) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T2の度数分布とヒストグラム
hsc5_T2_count <- dplyr::count(data, hsc5_T2)
knitr::kable(hsc5_T2_count) #テーブル化
| hsc5_T2 | n |
|---|---|
| 3 | 2 |
| 4 | 6 |
| 5 | 16 |
| 6 | 30 |
| 7 | 46 |
| NA | 14 |
u <- ggplot(data = data, mapping = aes(x = hsc5_T2, fill = factor(hsc5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(u) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T2の度数分布とヒストグラム
hsc6_T2_count <- dplyr::count(data, hsc6_T2)
knitr::kable(hsc6_T2_count) #テーブル化
| hsc6_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 8 |
| 3 | 8 |
| 4 | 13 |
| 5 | 40 |
| 6 | 20 |
| 7 | 10 |
| NA | 14 |
v <- ggplot(data = data, mapping = aes(x = hsc6_T2, fill = factor(hsc6_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(v) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T2の度数分布とヒストグラム
hsc7_T2_count <- dplyr::count(data, hsc7_T2)
knitr::kable(hsc7_T2_count) #テーブル化
| hsc7_T2 | n |
|---|---|
| 1 | 3 |
| 2 | 9 |
| 3 | 9 |
| 4 | 23 |
| 5 | 10 |
| 6 | 27 |
| 7 | 19 |
| NA | 14 |
w <- ggplot(data = data, mapping = aes(x = hsc7_T2, fill = factor(hsc7_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(w) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T2の度数分布とヒストグラム
hsc8_T2_count <- dplyr::count(data, hsc8_T2)
knitr::kable(hsc8_T2_count) #テーブル化
| hsc8_T2 | n |
|---|---|
| 2 | 11 |
| 3 | 16 |
| 4 | 23 |
| 5 | 31 |
| 6 | 13 |
| 7 | 5 |
| NA | 15 |
neko <- ggplot(data = data, mapping = aes(x = hsc8_T2, fill = factor(hsc8_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(neko) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T2の度数分布とヒストグラム
hsc9_T2_count <- dplyr::count(data, hsc9_T2)
knitr::kable(hsc9_T2_count) #テーブル化
| hsc9_T2 | n |
|---|---|
| 1 | 4 |
| 2 | 20 |
| 3 | 15 |
| 4 | 30 |
| 5 | 17 |
| 6 | 9 |
| 7 | 5 |
| NA | 14 |
y <- ggplot(data = data, mapping = aes(x = hsc9_T2, fill = factor(hsc9_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(y) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T2の度数分布とヒストグラム
hsc10_T2_count <- dplyr::count(data, hsc10_T2)
knitr::kable(hsc10_T2_count) #テーブル化
| hsc10_T2 | n |
|---|---|
| 2 | 1 |
| 3 | 2 |
| 4 | 1 |
| 5 | 12 |
| 6 | 31 |
| 7 | 53 |
| NA | 14 |
z <- ggplot(data = data, mapping = aes(x = hsc10_T2, fill = factor(hsc10_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(z) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T2の度数分布とヒストグラム
hsc11_T2_count <- dplyr::count(data, hsc11_T2)
knitr::kable(hsc11_T2_count) #テーブル化
| hsc11_T2 | n |
|---|---|
| 1 | 2 |
| 2 | 10 |
| 3 | 11 |
| 4 | 14 |
| 5 | 30 |
| 6 | 18 |
| 7 | 14 |
| NA | 15 |
aa <- ggplot(data = data, mapping = aes(x = hsc11_T2, fill = factor(hsc11_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T2の度数分布とヒストグラム
hsc12_T2_count <- dplyr::count(data, hsc12_T2)
knitr::kable(hsc12_T2_count) #テーブル化
| hsc12_T2 | n |
|---|---|
| 1 | 1 |
| 2 | 5 |
| 3 | 3 |
| 4 | 9 |
| 5 | 22 |
| 6 | 36 |
| 7 | 24 |
| NA | 14 |
bb <- ggplot(data = data, mapping = aes(x = hsc12_T2, fill = factor(hsc12_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T3の度数分布とヒストグラム
hsc1_T3_count <- dplyr::count(data, hsc1_T3)
knitr::kable(hsc1_T3_count) #テーブル化
| hsc1_T3 | n |
|---|---|
| 1 | 1 |
| 2 | 3 |
| 3 | 12 |
| 4 | 19 |
| 5 | 40 |
| 6 | 24 |
| 7 | 6 |
| NA | 9 |
cc <- ggplot(data = data, mapping = aes(x = hsc1_T3, fill = factor(hsc1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T3の度数分布とヒストグラム
hsc2_T3_count <- dplyr::count(data, hsc2_T3)
knitr::kable(hsc2_T3_count) #テーブル化
| hsc2_T3 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 9 |
| 4 | 11 |
| 5 | 28 |
| 6 | 35 |
| 7 | 15 |
| NA | 9 |
dd <- ggplot(data = data, mapping = aes(x = hsc2_T3, fill = factor(hsc2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T3の度数分布とヒストグラム
hsc3_T3_count <- dplyr::count(data, hsc3_T3)
knitr::kable(hsc3_T3_count) #テーブル化
| hsc3_T3 | n |
|---|---|
| 2 | 1 |
| 3 | 2 |
| 4 | 6 |
| 5 | 23 |
| 6 | 29 |
| 7 | 44 |
| NA | 9 |
ff <- ggplot(data = data, mapping = aes(x = hsc3_T3, fill = factor(hsc3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T3の度数分布とヒストグラム
hsc4_T3_count <- dplyr::count(data, hsc4_T3)
knitr::kable(hsc4_T3_count) #テーブル化
| hsc4_T3 | n |
|---|---|
| 1 | 3 |
| 2 | 7 |
| 3 | 3 |
| 4 | 17 |
| 5 | 37 |
| 6 | 24 |
| 7 | 14 |
| NA | 9 |
gg <- ggplot(data = data, mapping = aes(x = hsc4_T3, fill = factor(hsc4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T3の度数分布とヒストグラム
hsc5_T3_count <- dplyr::count(data, hsc5_T3)
knitr::kable(hsc5_T3_count) #テーブル化
| hsc5_T3 | n |
|---|---|
| 2 | 2 |
| 3 | 2 |
| 4 | 4 |
| 5 | 15 |
| 6 | 24 |
| 7 | 58 |
| NA | 9 |
hh <- ggplot(data = data, mapping = aes(x = hsc5_T3, fill = factor(hsc5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T3の度数分布とヒストグラム
hsc6_T3_count <- dplyr::count(data, hsc6_T3)
knitr::kable(hsc6_T3_count) #テーブル化
| hsc6_T3 | n |
|---|---|
| 1 | 2 |
| 2 | 2 |
| 3 | 5 |
| 4 | 14 |
| 5 | 31 |
| 6 | 33 |
| 7 | 17 |
| NA | 10 |
ii <- ggplot(data = data, mapping = aes(x = hsc6_T3, fill = factor(hsc6_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T3の度数分布とヒストグラム
hsc7_T3_count <- dplyr::count(data, hsc7_T3)
knitr::kable(hsc7_T3_count) #テーブル化
| hsc7_T3 | n |
|---|---|
| 1 | 5 |
| 2 | 11 |
| 3 | 8 |
| 4 | 22 |
| 5 | 13 |
| 6 | 23 |
| 7 | 23 |
| NA | 9 |
jj <- ggplot(data = data, mapping = aes(x = hsc7_T3, fill = factor(hsc7_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T3の度数分布とヒストグラム
hsc8_T3_count <- dplyr::count(data, hsc8_T3)
knitr::kable(hsc8_T3_count) #テーブル化
| hsc8_T3 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 15 |
| 4 | 23 |
| 5 | 29 |
| 6 | 18 |
| 7 | 13 |
| NA | 9 |
kk <- ggplot(data = data, mapping = aes(x = hsc8_T3, fill = factor(hsc8_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T3の度数分布とヒストグラム
hsc9_T3_count <- dplyr::count(data, hsc9_T3)
knitr::kable(hsc9_T3_count) #テーブル化
| hsc9_T3 | n |
|---|---|
| 1 | 6 |
| 2 | 15 |
| 3 | 16 |
| 4 | 29 |
| 5 | 25 |
| 6 | 10 |
| 7 | 4 |
| NA | 9 |
ll <- ggplot(data = data, mapping = aes(x = hsc9_T3, fill = factor(hsc9_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T3の度数分布とヒストグラム
hsc10_T3_count <- dplyr::count(data, hsc10_T3)
knitr::kable(hsc10_T3_count) #テーブル化
| hsc10_T3 | n |
|---|---|
| 3 | 1 |
| 4 | 3 |
| 5 | 10 |
| 6 | 29 |
| 7 | 62 |
| NA | 9 |
dog <- ggplot(data = data, mapping = aes(x = hsc10_T3, fill = factor(hsc10_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dog) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T3の度数分布とヒストグラム
hsc11_T3_count <- dplyr::count(data, hsc11_T3)
knitr::kable(hsc11_T3_count) #テーブル化
| hsc11_T3 | n |
|---|---|
| 1 | 3 |
| 2 | 4 |
| 3 | 10 |
| 4 | 22 |
| 5 | 25 |
| 6 | 25 |
| 7 | 16 |
| NA | 9 |
mm <- ggplot(data = data, mapping = aes(x = hsc11_T3, fill = factor(hsc11_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T3の度数分布とヒストグラム
hsc12_T3_count <- dplyr::count(data, hsc12_T3)
knitr::kable(hsc12_T3_count) #テーブル化
| hsc12_T3 | n |
|---|---|
| 2 | 1 |
| 3 | 2 |
| 4 | 9 |
| 5 | 27 |
| 6 | 39 |
| 7 | 27 |
| NA | 9 |
nn <- ggplot(data = data, mapping = aes(x = hsc12_T3, fill = factor(hsc12_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T4の度数分布とヒストグラム
hsc1_T4_count <- dplyr::count(data, hsc1_T4)
knitr::kable(hsc1_T4_count) #テーブル化
| hsc1_T4 | n |
|---|---|
| 1 | 1 |
| 2 | 2 |
| 3 | 15 |
| 4 | 17 |
| 5 | 41 |
| 6 | 27 |
| 7 | 3 |
| NA | 8 |
oo <- ggplot(data = data, mapping = aes(x = hsc1_T4, fill = factor(hsc1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(oo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T4の度数分布とヒストグラム
hsc2_T4_count <- dplyr::count(data, hsc2_T4)
knitr::kable(hsc2_T4_count) #テーブル化
| hsc2_T4 | n |
|---|---|
| 2 | 9 |
| 3 | 11 |
| 4 | 9 |
| 5 | 32 |
| 6 | 32 |
| 7 | 13 |
| NA | 8 |
pp <- ggplot(data = data, mapping = aes(x = hsc2_T4, fill = factor(hsc2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(pp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T4の度数分布とヒストグラム
hsc3_T4_count <- dplyr::count(data, hsc3_T4)
knitr::kable(hsc3_T4_count) #テーブル化
| hsc3_T4 | n |
|---|---|
| 2 | 2 |
| 3 | 2 |
| 4 | 9 |
| 5 | 21 |
| 6 | 29 |
| 7 | 43 |
| NA | 8 |
qq <- ggplot(data = data, mapping = aes(x = hsc3_T4, fill = factor(hsc3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T4の度数分布とヒストグラム
hsc4_T4_count <- dplyr::count(data, hsc4_T4)
knitr::kable(hsc4_T4_count) #テーブル化
| hsc4_T4 | n |
|---|---|
| 1 | 5 |
| 2 | 5 |
| 3 | 13 |
| 4 | 12 |
| 5 | 35 |
| 6 | 22 |
| 7 | 14 |
| NA | 8 |
rr <- ggplot(data = data, mapping = aes(x = hsc4_T4, fill = factor(hsc4_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T4の度数分布とヒストグラム
hsc5_T4_count <- dplyr::count(data, hsc5_T4)
knitr::kable(hsc5_T4_count) #テーブル化
| hsc5_T4 | n |
|---|---|
| 2 | 1 |
| 3 | 4 |
| 4 | 3 |
| 5 | 19 |
| 6 | 23 |
| 7 | 54 |
| NA | 10 |
ss <- ggplot(data = data, mapping = aes(x = hsc5_T4, fill = factor(hsc5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T4の度数分布とヒストグラム
hsc6_T4_count <- dplyr::count(data, hsc6_T4)
knitr::kable(hsc6_T4_count) #テーブル化
| hsc6_T4 | n |
|---|---|
| 1 | 2 |
| 2 | 8 |
| 3 | 8 |
| 4 | 9 |
| 5 | 38 |
| 6 | 29 |
| 7 | 12 |
| NA | 8 |
inu <- ggplot(data = data, mapping = aes(x = hsc6_T4, fill = factor(hsc6_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(inu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T4の度数分布とヒストグラム
hsc7_T4_count <- dplyr::count(data, hsc7_T4)
knitr::kable(hsc7_T4_count) #テーブル化
| hsc7_T4 | n |
|---|---|
| 1 | 4 |
| 2 | 9 |
| 3 | 11 |
| 4 | 16 |
| 5 | 18 |
| 6 | 27 |
| 7 | 21 |
| NA | 8 |
tt <- ggplot(data = data, mapping = aes(x = hsc7_T4, fill = factor(hsc7_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(tt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T4の度数分布とヒストグラム
hsc8_T4_count <- dplyr::count(data, hsc8_T4)
knitr::kable(hsc8_T4_count) #テーブル化
| hsc8_T4 | n |
|---|---|
| 1 | 2 |
| 2 | 5 |
| 3 | 17 |
| 4 | 20 |
| 5 | 34 |
| 6 | 17 |
| 7 | 11 |
| NA | 8 |
vv <- ggplot(data = data, mapping = aes(x = hsc8_T4, fill = factor(hsc8_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T4の度数分布とヒストグラム
hsc9_T4_count <- dplyr::count(data, hsc9_T4)
knitr::kable(hsc9_T4_count) #テーブル化
| hsc9_T4 | n |
|---|---|
| 1 | 6 |
| 2 | 13 |
| 3 | 19 |
| 4 | 30 |
| 5 | 24 |
| 6 | 11 |
| 7 | 3 |
| NA | 8 |
ww <- ggplot(data = data, mapping = aes(x = hsc9_T4, fill = factor(hsc9_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T4の度数分布とヒストグラム
hsc10_T4_count <- dplyr::count(data, hsc10_T4)
knitr::kable(hsc10_T4_count) #テーブル化
| hsc10_T4 | n |
|---|---|
| 2 | 1 |
| 3 | 1 |
| 4 | 3 |
| 5 | 11 |
| 6 | 30 |
| 7 | 60 |
| NA | 8 |
kame <- ggplot(data = data, mapping = aes(x = hsc10_T4, fill = factor(hsc10_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kame) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T4の度数分布とヒストグラム
hsc11_T4_count <- dplyr::count(data, hsc11_T4)
knitr::kable(hsc11_T4_count) #テーブル化
| hsc11_T4 | n |
|---|---|
| 1 | 2 |
| 2 | 10 |
| 3 | 11 |
| 4 | 18 |
| 5 | 28 |
| 6 | 22 |
| 7 | 15 |
| NA | 8 |
yy <- ggplot(data = data, mapping = aes(x = hsc11_T4, fill = factor(hsc11_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T4の度数分布とヒストグラム
hsc12_T4_count <- dplyr::count(data, hsc12_T4)
knitr::kable(hsc12_T4_count) #テーブル化
| hsc12_T4 | n |
|---|---|
| 1 | 1 |
| 2 | 5 |
| 3 | 2 |
| 4 | 7 |
| 5 | 31 |
| 6 | 31 |
| 7 | 29 |
| NA | 8 |
zz <- ggplot(data = data, mapping = aes(x = hsc12_T4, fill = factor(hsc12_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T1の度数分布とヒストグラム
wb1_T1_count <- dplyr::count(data, wb1_T1)
knitr::kable(wb1_T1_count) #テーブル化
| wb1_T1 | n |
|---|---|
| 0 | 1 |
| 1 | 6 |
| 2 | 27 |
| 3 | 39 |
| 4 | 31 |
| 5 | 10 |
aaa <- ggplot(data = data, mapping = aes(x = wb1_T1, fill = factor(wb1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T1の度数分布とヒストグラム
wb2_T1_count <- dplyr::count(data, wb2_T1)
knitr::kable(wb2_T1_count) #テーブル化
| wb2_T1 | n |
|---|---|
| 0 | 2 |
| 1 | 12 |
| 2 | 31 |
| 3 | 40 |
| 4 | 23 |
| 5 | 6 |
bbb <- ggplot(data = data, mapping = aes(x = wb2_T1, fill = factor(wb2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T1の度数分布とヒストグラム
wb3_T1_count <- dplyr::count(data, wb3_T1)
knitr::kable(wb3_T1_count) #テーブル化
| wb3_T1 | n |
|---|---|
| 0 | 2 |
| 1 | 11 |
| 2 | 26 |
| 3 | 36 |
| 4 | 28 |
| 5 | 10 |
| NA | 1 |
ccc <- ggplot(data = data, mapping = aes(x = wb3_T1, fill = factor(wb3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T1の度数分布とヒストグラム
wb4_T1_count <- dplyr::count(data, wb4_T1)
knitr::kable(wb4_T1_count) #テーブル化
| wb4_T1 | n |
|---|---|
| 0 | 4 |
| 1 | 35 |
| 2 | 34 |
| 3 | 27 |
| 4 | 9 |
| 5 | 5 |
ddd <- ggplot(data = data, mapping = aes(x = wb4_T1, fill = factor(wb4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T1の度数分布とヒストグラム
wb5_T1_count <- dplyr::count(data, wb5_T1)
knitr::kable(wb5_T1_count) #テーブル化
| wb5_T1 | n |
|---|---|
| 0 | 3 |
| 1 | 15 |
| 2 | 29 |
| 3 | 38 |
| 4 | 22 |
| 5 | 7 |
eee <- ggplot(data = data, mapping = aes(x = wb5_T1, fill = factor(wb5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T2の度数分布とヒストグラム
wb1_T2_count <- dplyr::count(data, wb1_T2)
knitr::kable(wb1_T2_count) #テーブル化
| wb1_T2 | n |
|---|---|
| 1 | 5 |
| 2 | 23 |
| 3 | 30 |
| 4 | 33 |
| 5 | 9 |
| NA | 14 |
fff <- ggplot(data = data, mapping = aes(x = wb1_T2, fill = factor(wb1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(fff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T2の度数分布とヒストグラム
wb2_T2_count <- dplyr::count(data, wb2_T2)
knitr::kable(wb2_T2_count) #テーブル化
| wb2_T2 | n |
|---|---|
| 1 | 7 |
| 2 | 23 |
| 3 | 28 |
| 4 | 32 |
| 5 | 9 |
| NA | 15 |
ggg <- ggplot(data = data, mapping = aes(x = wb2_T2, fill = factor(wb2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T2の度数分布とヒストグラム
wb3_T2_count <- dplyr::count(data, wb3_T2)
knitr::kable(wb3_T2_count) #テーブル化
| wb3_T2 | n |
|---|---|
| 1 | 10 |
| 2 | 16 |
| 3 | 36 |
| 4 | 25 |
| 5 | 13 |
| NA | 14 |
hhh <- ggplot(data = data, mapping = aes(x = wb3_T2, fill = factor(wb3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T2の度数分布とヒストグラム
wb4_T2_count <- dplyr::count(data, wb4_T2)
knitr::kable(wb4_T2_count) #テーブル化
| wb4_T2 | n |
|---|---|
| 0 | 2 |
| 1 | 26 |
| 2 | 29 |
| 3 | 23 |
| 4 | 12 |
| 5 | 8 |
| NA | 14 |
iii <- ggplot(data = data, mapping = aes(x = wb4_T2, fill = factor(wb4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T2の度数分布とヒストグラム
wb5_T2_count <- dplyr::count(data, wb5_T2)
knitr::kable(wb5_T2_count) #テーブル化
| wb5_T2 | n |
|---|---|
| 1 | 12 |
| 2 | 26 |
| 3 | 28 |
| 4 | 21 |
| 5 | 13 |
| NA | 14 |
jjj <- ggplot(data = data, mapping = aes(x = wb5_T2, fill = factor(wb5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T3の度数分布とヒストグラム
wb1_T3_count <- dplyr::count(data, wb1_T3)
knitr::kable(wb1_T3_count) #テーブル化
| wb1_T3 | n |
|---|---|
| 0 | 1 |
| 1 | 6 |
| 2 | 18 |
| 3 | 36 |
| 4 | 28 |
| 5 | 16 |
| NA | 9 |
kkk <- ggplot(data = data, mapping = aes(x = wb1_T3, fill = factor(wb1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T3の度数分布とヒストグラム
wb2_T3_count <- dplyr::count(data, wb2_T3)
knitr::kable(wb2_T3_count) #テーブル化
| wb2_T3 | n |
|---|---|
| 0 | 1 |
| 1 | 12 |
| 2 | 22 |
| 3 | 32 |
| 4 | 27 |
| 5 | 11 |
| NA | 9 |
lll <- ggplot(data = data, mapping = aes(x = wb2_T3, fill = factor(wb2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(lll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T3の度数分布とヒストグラム
wb3_T3_count <- dplyr::count(data, wb3_T3)
knitr::kable(wb1_T3_count) #テーブル化
| wb1_T3 | n |
|---|---|
| 0 | 1 |
| 1 | 6 |
| 2 | 18 |
| 3 | 36 |
| 4 | 28 |
| 5 | 16 |
| NA | 9 |
mmm <- ggplot(data = data, mapping = aes(x = wb3_T3, fill = factor(wb3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T3の度数分布とヒストグラム
wb4_T3_count <- dplyr::count(data, wb4_T3)
knitr::kable(wb4_T3_count) #テーブル化
| wb4_T3 | n |
|---|---|
| 0 | 6 |
| 1 | 20 |
| 2 | 40 |
| 3 | 18 |
| 4 | 15 |
| 5 | 6 |
| NA | 9 |
nnn <- ggplot(data = data, mapping = aes(x = wb4_T3, fill = factor(wb4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T3の度数分布とヒストグラム
wb5_T3_count <- dplyr::count(data, wb5_T3)
knitr::kable(wb5_T3_count) #テーブル化
| wb5_T3 | n |
|---|---|
| 0 | 1 |
| 1 | 16 |
| 2 | 27 |
| 3 | 38 |
| 4 | 16 |
| 5 | 7 |
| NA | 9 |
ooo <- ggplot(data = data, mapping = aes(x = wb5_T3, fill = factor(wb5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T4の度数分布とヒストグラム
wb1_T4_count <- dplyr::count(data, wb1_T4)
knitr::kable(wb1_T4_count) #テーブル化
| wb1_T4 | n |
|---|---|
| 0 | 1 |
| 1 | 10 |
| 2 | 17 |
| 3 | 34 |
| 4 | 34 |
| 5 | 10 |
| NA | 8 |
ppp <- ggplot(data = data, mapping = aes(x = wb1_T4, fill = factor(wb1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T4の度数分布とヒストグラム
wb2_T4_count <- dplyr::count(data, wb2_T4)
knitr::kable(wb2_T4_count) #テーブル化
| wb2_T4 | n |
|---|---|
| 0 | 3 |
| 1 | 14 |
| 2 | 24 |
| 3 | 32 |
| 4 | 21 |
| 5 | 12 |
| NA | 8 |
qqq <- ggplot(data = data, mapping = aes(x = wb2_T4, fill = factor(wb2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T4の度数分布とヒストグラム
wb3_T4_count <- dplyr::count(data, wb3_T4)
knitr::kable(wb3_T4_count) #テーブル化
| wb3_T4 | n |
|---|---|
| 0 | 1 |
| 1 | 9 |
| 2 | 25 |
| 3 | 31 |
| 4 | 23 |
| 5 | 17 |
| NA | 8 |
rrr <- ggplot(data = data, mapping = aes(x = wb3_T4, fill = factor(wb3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T4の度数分布とヒストグラム
wb5_T4_count <- dplyr::count(data, wb5_T4)
knitr::kable(wb5_T4_count) #テーブル化
| wb5_T4 | n |
|---|---|
| 0 | 4 |
| 1 | 12 |
| 2 | 25 |
| 3 | 30 |
| 4 | 20 |
| 5 | 15 |
| NA | 8 |
sss <- ggplot(data = data, mapping = aes(x = wb5_T4, fill = factor(wb5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(sss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T1の度数分布とヒストグラム
event1_T1_count <- dplyr::count(data, ev1_T1)
knitr::kable(event1_T1_count) #テーブル化
| ev1_T1 | n |
|---|---|
| -3 | 16 |
| -2 | 11 |
| -1 | 9 |
| 0 | 3 |
| 1 | 8 |
| 2 | 11 |
| 3 | 55 |
| NA | 1 |
ttt <- ggplot(data = data, mapping = aes(x = ev1_T1, fill = factor(ev1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T1の度数分布とヒストグラム
event2_T1_count <- dplyr::count(data, ev2_T1)
knitr::kable(event2_T1_count) #テーブル化
| ev2_T1 | n |
|---|---|
| -3 | 21 |
| -2 | 16 |
| -1 | 8 |
| 0 | 5 |
| 1 | 2 |
| 2 | 19 |
| 3 | 42 |
| NA | 1 |
uuu <- ggplot(data = data, mapping = aes(x = ev2_T1, fill = factor(ev2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(uuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T2の度数分布とヒストグラム
event1_T2_count <- dplyr::count(data, ev1_T2)
knitr::kable(event1_T2_count) #テーブル化
| ev1_T2 | n |
|---|---|
| -3 | 17 |
| -2 | 7 |
| -1 | 4 |
| 0 | 4 |
| 1 | 6 |
| 2 | 13 |
| 3 | 47 |
| NA | 16 |
vvv <- ggplot(data = data, mapping = aes(x = ev1_T2, fill = factor(ev1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T2の度数分布とヒストグラム
event2_T2_count <- dplyr::count(data, ev2_T2)
knitr::kable(event2_T2_count) #テーブル化
| ev2_T2 | n |
|---|---|
| -3 | 20 |
| -2 | 15 |
| -1 | 5 |
| 0 | 1 |
| 1 | 8 |
| 2 | 17 |
| 3 | 29 |
| NA | 19 |
www <- ggplot(data = data, mapping = aes(x = ev2_T2, fill = factor(ev2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(www) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T3の度数分布とヒストグラム
event1_T3_count <- dplyr::count(data, ev1_T3)
knitr::kable(event1_T3_count) #テーブル化
| ev1_T3 | n |
|---|---|
| -3 | 19 |
| -2 | 6 |
| -1 | 10 |
| 0 | 6 |
| 1 | 4 |
| 2 | 11 |
| 3 | 47 |
| NA | 11 |
usagi <- ggplot(data = data, mapping = aes(x = ev1_T3, fill = factor(ev1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(usagi) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T3の度数分布とヒストグラム
event2_T3_count <- dplyr::count(data, ev2_T3)
knitr::kable(event2_T3_count) #テーブル化
| ev2_T3 | n |
|---|---|
| -3 | 22 |
| -2 | 12 |
| -1 | 4 |
| 0 | 5 |
| 1 | 13 |
| 2 | 13 |
| 3 | 34 |
| NA | 11 |
yyy <- ggplot(data = data, mapping = aes(x = ev2_T3, fill = factor(ev2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T4の度数分布とヒストグラム
event1_T4_count <- dplyr::count(data, ev1_T4)
knitr::kable(event1_T4_count) #テーブル化
| ev1_T4 | n |
|---|---|
| -3 | 15 |
| -2 | 5 |
| -1 | 3 |
| 0 | 4 |
| 1 | 10 |
| 2 | 15 |
| 3 | 49 |
| NA | 13 |
zzz <- ggplot(data = data, mapping = aes(x = ev1_T4, fill = factor(ev1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T4の度数分布とヒストグラム
event2_T4_count <- dplyr::count(data, ev2_T4)
knitr::kable(event2_T4_count) #テーブル化
| ev2_T4 | n |
|---|---|
| -3 | 15 |
| -2 | 13 |
| -1 | 10 |
| 0 | 9 |
| 1 | 9 |
| 2 | 14 |
| 3 | 31 |
| NA | 13 |
aaaa <- ggplot(data = data, mapping = aes(x = ev2_T4, fill = factor(ev2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T1の度数分布とヒストグラム
eoe_T1_count <- dplyr::count(data, eoe_T1)
knitr::kable(eoe_T1_count) #テーブル化
| eoe_T1 | n |
|---|---|
| 1.8 | 2 |
| 2.2 | 1 |
| 2.4 | 2 |
| 2.6 | 1 |
| 2.8 | 1 |
| 3.0 | 1 |
| 3.2 | 3 |
| 3.4 | 5 |
| 3.6 | 2 |
| 3.8 | 10 |
| 4.0 | 6 |
| 4.2 | 14 |
| 4.4 | 6 |
| 4.6 | 7 |
| 4.8 | 11 |
| 5.0 | 9 |
| 5.2 | 10 |
| 5.4 | 5 |
| 5.6 | 6 |
| 5.8 | 5 |
| 6.0 | 4 |
| 6.4 | 3 |
bbbb <- ggplot(data = data, mapping = aes(x = eoe_T1, fill = factor(eoe_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T2の度数分布とヒストグラム
eoe_T2_count <- dplyr::count(data, eoe_T2)
knitr::kable(eoe_T2_count) #テーブル化
| eoe_T2 | n |
|---|---|
| 1.8 | 2 |
| 2.0 | 1 |
| 2.4 | 1 |
| 2.6 | 2 |
| 2.8 | 2 |
| 3.0 | 1 |
| 3.2 | 2 |
| 3.4 | 2 |
| 3.6 | 2 |
| 3.8 | 4 |
| 4.0 | 5 |
| 4.2 | 8 |
| 4.4 | 6 |
| 4.6 | 9 |
| 4.8 | 16 |
| 5.0 | 3 |
| 5.2 | 7 |
| 5.4 | 5 |
| 5.6 | 9 |
| 5.8 | 2 |
| 6.0 | 3 |
| 6.2 | 4 |
| 6.4 | 1 |
| 6.6 | 1 |
| 6.8 | 1 |
| NA | 15 |
cccc <- ggplot(data = data, mapping = aes(x = eoe_T2, fill = factor(eoe_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T3の度数分布とヒストグラム
eoe_T3_count <- dplyr::count(data, eoe_T3)
knitr::kable(eoe_T3_count) #テーブル化
| eoe_T3 | n |
|---|---|
| 2.0 | 1 |
| 2.2 | 1 |
| 2.4 | 1 |
| 2.6 | 1 |
| 3.0 | 2 |
| 3.2 | 1 |
| 3.6 | 1 |
| 3.8 | 4 |
| 4.0 | 3 |
| 4.2 | 5 |
| 4.4 | 7 |
| 4.6 | 10 |
| 4.8 | 8 |
| 5.0 | 14 |
| 5.2 | 11 |
| 5.4 | 11 |
| 5.6 | 7 |
| 5.8 | 3 |
| 6.0 | 2 |
| 6.2 | 2 |
| 6.4 | 5 |
| 6.6 | 2 |
| 7.0 | 2 |
| NA | 10 |
dddd <- ggplot(data = data, mapping = aes(x = eoe_T3, fill = factor(eoe_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T4の度数分布とヒストグラム
eoe_T4_count <- dplyr::count(data, eoe_T4)
knitr::kable(eoe_T4_count) #テーブル化
| eoe_T4 | n |
|---|---|
| 1.0 | 1 |
| 2.0 | 1 |
| 2.2 | 1 |
| 2.4 | 1 |
| 2.6 | 1 |
| 2.8 | 3 |
| 3.0 | 2 |
| 3.2 | 3 |
| 3.4 | 1 |
| 3.6 | 1 |
| 3.8 | 3 |
| 4.0 | 6 |
| 4.2 | 2 |
| 4.4 | 7 |
| 4.6 | 13 |
| 4.8 | 7 |
| 5.0 | 7 |
| 5.2 | 10 |
| 5.4 | 7 |
| 5.6 | 11 |
| 5.8 | 6 |
| 6.0 | 5 |
| 6.2 | 2 |
| 6.4 | 2 |
| 6.6 | 1 |
| 6.8 | 1 |
| 7.0 | 1 |
| NA | 8 |
eeee <- ggplot(data = data, mapping = aes(x = eoe_T4, fill = factor(eoe_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eeee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T1の度数分布とヒストグラム
lst_T1_count <- dplyr::count(data, lst_T1)
knitr::kable(lst_T1_count) #テーブル化
| lst_T1 | n |
|---|---|
| 1.5 | 2 |
| 2.0 | 3 |
| 2.5 | 3 |
| 3.0 | 7 |
| 3.5 | 15 |
| 4.0 | 8 |
| 4.5 | 14 |
| 5.0 | 15 |
| 5.5 | 16 |
| 6.0 | 10 |
| 6.5 | 9 |
| 7.0 | 12 |
ffff <- ggplot(data = data, mapping = aes(x = lst_T1, fill = factor(lst_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ffff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T2の度数分布とヒストグラム
lst_T2_count <- dplyr::count(data, lst_T2)
knitr::kable(lst_T2_count) #テーブル化
| lst_T2 | n |
|---|---|
| 1.0 | 1 |
| 2.0 | 6 |
| 2.5 | 4 |
| 3.0 | 5 |
| 3.5 | 6 |
| 4.0 | 9 |
| 4.5 | 11 |
| 5.0 | 18 |
| 5.5 | 13 |
| 6.0 | 12 |
| 6.5 | 7 |
| 7.0 | 7 |
| NA | 15 |
gggg <- ggplot(data = data, mapping = aes(x = lst_T2, fill = factor(lst_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T3の度数分布とヒストグラム
lst_T3_count <- dplyr::count(data, lst_T3)
knitr::kable(lst_T3_count) #テーブル化
| lst_T3 | n |
|---|---|
| 1.0 | 1 |
| 1.5 | 1 |
| 2.0 | 2 |
| 2.5 | 1 |
| 3.0 | 8 |
| 3.5 | 5 |
| 4.0 | 13 |
| 4.5 | 7 |
| 5.0 | 17 |
| 5.5 | 13 |
| 6.0 | 19 |
| 6.5 | 7 |
| 7.0 | 11 |
| NA | 9 |
hhhh <- ggplot(data = data, mapping = aes(x = lst_T3, fill = factor(lst_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T4の度数分布とヒストグラム
lst_T4_count <- dplyr::count(data, lst_T4)
knitr::kable(lst_T4_count) #テーブル化
| lst_T4 | n |
|---|---|
| 2.0 | 6 |
| 2.5 | 3 |
| 3.0 | 9 |
| 3.5 | 4 |
| 4.0 | 10 |
| 4.5 | 8 |
| 5.0 | 22 |
| 5.5 | 11 |
| 6.0 | 18 |
| 6.5 | 6 |
| 7.0 | 9 |
| NA | 8 |
iiii <- ggplot(data = data, mapping = aes(x = lst_T4, fill = factor(lst_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iiii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T1の度数分布とヒストグラム
aes_T1_count <- dplyr::count(data, aes_T1)
knitr::kable(aes_T1_count) #テーブル化
| aes_T1 | n |
|---|---|
| 3.25 | 1 |
| 4.25 | 1 |
| 4.50 | 2 |
| 4.75 | 6 |
| 5.00 | 6 |
| 5.25 | 15 |
| 5.50 | 9 |
| 5.75 | 22 |
| 6.00 | 21 |
| 6.25 | 11 |
| 6.50 | 9 |
| 6.75 | 5 |
| 7.00 | 5 |
| NA | 1 |
jjjj <- ggplot(data = data, mapping = aes(x = aes_T1, fill = factor(aes_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T2の度数分布とヒストグラム
aes_T2_count <- dplyr::count(data, aes_T2)
knitr::kable(aes_T2_count) #テーブル化
| aes_T2 | n |
|---|---|
| 2.50 | 1 |
| 3.50 | 1 |
| 4.00 | 1 |
| 4.50 | 3 |
| 4.75 | 3 |
| 5.00 | 10 |
| 5.25 | 12 |
| 5.50 | 15 |
| 5.75 | 11 |
| 6.00 | 13 |
| 6.25 | 15 |
| 6.50 | 6 |
| 6.75 | 6 |
| 7.00 | 3 |
| NA | 14 |
kkkk <- ggplot(data = data, mapping = aes(x = aes_T2, fill = factor(aes_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T3の度数分布とヒストグラム
aes_T3_count <- dplyr::count(data, aes_T3)
knitr::kable(aes_T3_count) #テーブル化
| aes_T3 | n |
|---|---|
| 4.00 | 2 |
| 4.25 | 1 |
| 4.50 | 2 |
| 4.75 | 1 |
| 5.00 | 10 |
| 5.25 | 9 |
| 5.50 | 13 |
| 5.75 | 15 |
| 6.00 | 9 |
| 6.25 | 17 |
| 6.50 | 14 |
| 6.75 | 7 |
| 7.00 | 5 |
| NA | 9 |
llll <- ggplot(data = data, mapping = aes(x = aes_T3, fill = factor(aes_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(llll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T4の度数分布とヒストグラム
aes_T4_count <- dplyr::count(data, aes_T4)
knitr::kable(aes_T4_count) #テーブル化
| aes_T4 | n |
|---|---|
| 2.75 | 2 |
| 3.50 | 1 |
| 4.25 | 2 |
| 4.50 | 2 |
| 4.75 | 4 |
| 5.00 | 5 |
| 5.25 | 10 |
| 5.50 | 12 |
| 5.75 | 10 |
| 6.00 | 16 |
| 6.25 | 13 |
| 6.50 | 12 |
| 6.75 | 14 |
| 7.00 | 1 |
| NA | 10 |
mmmm <- ggplot(data = data, mapping = aes(x = aes_T4, fill = factor(aes_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T1の度数分布とヒストグラム
hsc_T1_count <- dplyr::count(data, hsc_T1)
knitr::kable(hsc_T1_count) #テーブル化
| hsc_T1 | n |
|---|---|
| 3.183333 | 1 |
| 3.300000 | 1 |
| 3.383333 | 1 |
| 3.400000 | 1 |
| 3.416667 | 1 |
| 3.650000 | 1 |
| 3.766667 | 1 |
| 3.933333 | 1 |
| 3.966667 | 1 |
| 3.983333 | 1 |
| 4.016667 | 1 |
| 4.066667 | 1 |
| 4.116667 | 1 |
| 4.150000 | 1 |
| 4.183333 | 1 |
| 4.266667 | 1 |
| 4.283333 | 1 |
| 4.316667 | 1 |
| 4.350000 | 2 |
| 4.383333 | 1 |
| 4.433333 | 1 |
| 4.450000 | 1 |
| 4.483333 | 3 |
| 4.500000 | 2 |
| 4.516667 | 1 |
| 4.633333 | 1 |
| 4.650000 | 1 |
| 4.683333 | 1 |
| 4.733333 | 1 |
| 4.750000 | 2 |
| 4.766667 | 2 |
| 4.800000 | 1 |
| 4.816667 | 1 |
| 4.850000 | 5 |
| 4.866667 | 1 |
| 4.883333 | 1 |
| 4.916667 | 1 |
| 4.933333 | 1 |
| 5.000000 | 2 |
| 5.016667 | 1 |
| 5.050000 | 1 |
| 5.066667 | 1 |
| 5.116667 | 1 |
| 5.150000 | 1 |
| 5.166667 | 2 |
| 5.200000 | 1 |
| 5.216667 | 2 |
| 5.233333 | 1 |
| 5.250000 | 1 |
| 5.283333 | 2 |
| 5.300000 | 1 |
| 5.316667 | 3 |
| 5.333333 | 2 |
| 5.366667 | 1 |
| 5.383333 | 1 |
| 5.416667 | 1 |
| 5.433333 | 2 |
| 5.450000 | 1 |
| 5.466667 | 1 |
| 5.483333 | 3 |
| 5.500000 | 1 |
| 5.516667 | 2 |
| 5.533333 | 1 |
| 5.566667 | 3 |
| 5.583333 | 1 |
| 5.616667 | 3 |
| 5.666667 | 1 |
| 5.683333 | 1 |
| 5.700000 | 1 |
| 5.733333 | 2 |
| 5.766667 | 1 |
| 5.800000 | 4 |
| 5.833333 | 1 |
| 5.850000 | 1 |
| 5.900000 | 1 |
| 5.916667 | 1 |
| 6.066667 | 1 |
| 6.083333 | 1 |
| 6.116667 | 1 |
| 6.133333 | 1 |
| 6.300000 | 1 |
| 6.383333 | 1 |
| 6.466667 | 1 |
| 6.600000 | 1 |
| NA | 1 |
nnnn <- ggplot(data = data, mapping = aes(x = hsc_T1, fill = factor(hsc_T1))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(nnnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T2の度数分布とヒストグラム
hsc_T2_count <- dplyr::count(data, hsc_T2)
knitr::kable(hsc_T2_count) #テーブル化
| hsc_T2 | n |
|---|---|
| 2.533333 | 1 |
| 2.916667 | 1 |
| 3.066667 | 1 |
| 3.600000 | 1 |
| 3.616667 | 1 |
| 3.716667 | 1 |
| 3.850000 | 1 |
| 4.000000 | 1 |
| 4.016667 | 1 |
| 4.033333 | 1 |
| 4.050000 | 1 |
| 4.100000 | 2 |
| 4.166667 | 2 |
| 4.233333 | 1 |
| 4.316667 | 1 |
| 4.400000 | 1 |
| 4.433333 | 1 |
| 4.466667 | 1 |
| 4.500000 | 1 |
| 4.516667 | 3 |
| 4.533333 | 1 |
| 4.550000 | 2 |
| 4.566667 | 1 |
| 4.633333 | 1 |
| 4.650000 | 1 |
| 4.733333 | 2 |
| 4.766667 | 1 |
| 4.783333 | 2 |
| 4.866667 | 1 |
| 4.883333 | 2 |
| 4.900000 | 1 |
| 4.933333 | 2 |
| 4.950000 | 1 |
| 4.983333 | 1 |
| 5.000000 | 1 |
| 5.016667 | 1 |
| 5.066667 | 1 |
| 5.100000 | 1 |
| 5.116667 | 1 |
| 5.150000 | 2 |
| 5.183333 | 2 |
| 5.200000 | 2 |
| 5.233333 | 2 |
| 5.250000 | 1 |
| 5.283333 | 1 |
| 5.300000 | 1 |
| 5.333333 | 1 |
| 5.350000 | 1 |
| 5.383333 | 1 |
| 5.416667 | 2 |
| 5.433333 | 2 |
| 5.450000 | 1 |
| 5.466667 | 1 |
| 5.516667 | 1 |
| 5.533333 | 1 |
| 5.616667 | 2 |
| 5.633333 | 1 |
| 5.666667 | 2 |
| 5.683333 | 3 |
| 5.700000 | 3 |
| 5.766667 | 1 |
| 5.783333 | 1 |
| 5.800000 | 1 |
| 5.816667 | 1 |
| 5.866667 | 1 |
| 5.883333 | 1 |
| 5.950000 | 1 |
| 6.016667 | 2 |
| 6.066667 | 2 |
| 6.150000 | 1 |
| 6.233333 | 2 |
| 6.283333 | 1 |
| 6.683333 | 1 |
| 6.800000 | 1 |
| NA | 16 |
oooo <- ggplot(data = data, mapping = aes(x = hsc_T2, fill = factor(hsc_T2))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(oooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T3の度数分布とヒストグラム
hsc_T3_count <- dplyr::count(data, hsc_T3)
knitr::kable(hsc_T3_count) #テーブル化
| hsc_T3 | n |
|---|---|
| 3.383333 | 1 |
| 3.483333 | 1 |
| 3.500000 | 1 |
| 3.766667 | 1 |
| 3.916667 | 1 |
| 3.950000 | 1 |
| 4.000000 | 1 |
| 4.016667 | 1 |
| 4.083333 | 1 |
| 4.150000 | 1 |
| 4.216667 | 1 |
| 4.283333 | 1 |
| 4.400000 | 1 |
| 4.433333 | 1 |
| 4.450000 | 1 |
| 4.483333 | 1 |
| 4.533333 | 1 |
| 4.566667 | 2 |
| 4.700000 | 2 |
| 4.766667 | 2 |
| 4.800000 | 1 |
| 4.866667 | 2 |
| 4.883333 | 1 |
| 4.916667 | 2 |
| 4.933333 | 1 |
| 4.950000 | 1 |
| 4.966667 | 2 |
| 4.983333 | 2 |
| 5.000000 | 1 |
| 5.033333 | 2 |
| 5.050000 | 1 |
| 5.100000 | 2 |
| 5.116667 | 1 |
| 5.133333 | 4 |
| 5.166667 | 2 |
| 5.183333 | 1 |
| 5.233333 | 1 |
| 5.250000 | 1 |
| 5.300000 | 1 |
| 5.316667 | 1 |
| 5.333333 | 2 |
| 5.400000 | 2 |
| 5.416667 | 4 |
| 5.433333 | 1 |
| 5.483333 | 2 |
| 5.500000 | 1 |
| 5.516667 | 1 |
| 5.533333 | 1 |
| 5.550000 | 1 |
| 5.583333 | 1 |
| 5.616667 | 1 |
| 5.633333 | 1 |
| 5.666667 | 1 |
| 5.683333 | 2 |
| 5.700000 | 1 |
| 5.716667 | 1 |
| 5.733333 | 2 |
| 5.800000 | 2 |
| 5.816667 | 1 |
| 5.883333 | 1 |
| 5.950000 | 1 |
| 5.983333 | 1 |
| 6.000000 | 1 |
| 6.016667 | 1 |
| 6.033333 | 1 |
| 6.050000 | 1 |
| 6.083333 | 1 |
| 6.100000 | 1 |
| 6.116667 | 1 |
| 6.133333 | 2 |
| 6.150000 | 1 |
| 6.166667 | 1 |
| 6.216667 | 1 |
| 6.333333 | 1 |
| 6.366667 | 1 |
| 6.383333 | 1 |
| 6.450000 | 2 |
| 6.566667 | 1 |
| 6.800000 | 1 |
| 6.916667 | 1 |
| NA | 10 |
pppp <- ggplot(data = data, mapping = aes(x = hsc_T3, fill = factor(hsc_T3))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(pppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T4の度数分布とヒストグラム
hsc_T4_count <- dplyr::count(data, hsc_T4)
knitr::kable(hsc_T4_count) #テーブル化
| hsc_T4 | n |
|---|---|
| 2.616667 | 1 |
| 3.083333 | 1 |
| 3.183333 | 1 |
| 3.583333 | 1 |
| 3.633333 | 1 |
| 3.833333 | 1 |
| 3.850000 | 1 |
| 3.916667 | 1 |
| 3.933333 | 2 |
| 4.066667 | 1 |
| 4.233333 | 2 |
| 4.250000 | 1 |
| 4.333333 | 2 |
| 4.350000 | 1 |
| 4.366667 | 1 |
| 4.516667 | 1 |
| 4.533333 | 1 |
| 4.616667 | 3 |
| 4.633333 | 2 |
| 4.650000 | 1 |
| 4.666667 | 1 |
| 4.716667 | 1 |
| 4.750000 | 1 |
| 4.766667 | 1 |
| 4.783333 | 3 |
| 4.800000 | 1 |
| 4.833333 | 2 |
| 4.850000 | 1 |
| 4.866667 | 2 |
| 4.883333 | 1 |
| 4.900000 | 1 |
| 4.916667 | 1 |
| 4.933333 | 1 |
| 4.950000 | 1 |
| 5.000000 | 1 |
| 5.033333 | 1 |
| 5.100000 | 1 |
| 5.116667 | 2 |
| 5.150000 | 1 |
| 5.166667 | 1 |
| 5.200000 | 1 |
| 5.250000 | 1 |
| 5.300000 | 2 |
| 5.316667 | 2 |
| 5.333333 | 1 |
| 5.350000 | 1 |
| 5.366667 | 1 |
| 5.383333 | 1 |
| 5.400000 | 1 |
| 5.416667 | 1 |
| 5.433333 | 1 |
| 5.466667 | 1 |
| 5.483333 | 1 |
| 5.533333 | 2 |
| 5.550000 | 2 |
| 5.566667 | 1 |
| 5.633333 | 1 |
| 5.683333 | 2 |
| 5.700000 | 1 |
| 5.716667 | 1 |
| 5.733333 | 1 |
| 5.750000 | 2 |
| 5.766667 | 3 |
| 5.783333 | 2 |
| 5.816667 | 1 |
| 5.850000 | 1 |
| 5.866667 | 1 |
| 5.883333 | 1 |
| 5.900000 | 2 |
| 5.966667 | 1 |
| 5.983333 | 1 |
| 6.050000 | 1 |
| 6.116667 | 1 |
| 6.183333 | 1 |
| 6.250000 | 2 |
| 6.283333 | 1 |
| 6.416667 | 1 |
| 6.433333 | 1 |
| 6.450000 | 1 |
| 6.483333 | 1 |
| 6.550000 | 1 |
| 7.000000 | 1 |
| NA | 10 |
qqqq <- ggplot(data = data, mapping = aes(x = hsc_T4, fill = factor(hsc_T4))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(qqqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T1の度数分布とヒストグラム
wb_T1_count <- dplyr::count(data, wb_T1)
knitr::kable(wb_T1_count) #テーブル化
| wb_T1 | n |
|---|---|
| 0.6 | 1 |
| 0.8 | 1 |
| 1.2 | 1 |
| 1.4 | 5 |
| 1.6 | 5 |
| 1.8 | 6 |
| 2.0 | 7 |
| 2.2 | 11 |
| 2.4 | 5 |
| 2.6 | 11 |
| 2.8 | 11 |
| 3.0 | 14 |
| 3.2 | 7 |
| 3.4 | 7 |
| 3.6 | 7 |
| 3.8 | 5 |
| 4.0 | 3 |
| 4.2 | 3 |
| 4.4 | 1 |
| 4.6 | 1 |
| 5.0 | 1 |
| NA | 1 |
rrrr <- ggplot(data = data, mapping = aes(x = wb_T1, fill = factor(wb_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(rrrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T2の度数分布とヒストグラム
wb_T2_count <- dplyr::count(data, wb_T2)
knitr::kable(wb_T2_count) #テーブル化
| wb_T2 | n |
|---|---|
| 1.0 | 1 |
| 1.2 | 1 |
| 1.4 | 1 |
| 1.6 | 5 |
| 1.8 | 6 |
| 2.0 | 4 |
| 2.2 | 7 |
| 2.4 | 7 |
| 2.6 | 8 |
| 2.8 | 2 |
| 3.0 | 15 |
| 3.2 | 10 |
| 3.4 | 7 |
| 3.6 | 2 |
| 3.8 | 7 |
| 4.0 | 4 |
| 4.2 | 2 |
| 4.4 | 5 |
| 4.6 | 2 |
| 5.0 | 3 |
| NA | 15 |
ssss <- ggplot(data = data, mapping = aes(x = wb_T2, fill = factor(wb_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(ssss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T3の度数分布とヒストグラム
wb_T3_count <- dplyr::count(data, wb_T3)
knitr::kable(wb_T3_count) #テーブル化
| wb_T3 | n |
|---|---|
| 0.0 | 1 |
| 0.8 | 1 |
| 1.0 | 2 |
| 1.4 | 3 |
| 1.6 | 1 |
| 1.8 | 5 |
| 2.0 | 5 |
| 2.2 | 8 |
| 2.4 | 10 |
| 2.6 | 7 |
| 2.8 | 10 |
| 3.0 | 12 |
| 3.2 | 8 |
| 3.4 | 5 |
| 3.6 | 4 |
| 3.8 | 10 |
| 4.0 | 5 |
| 4.4 | 1 |
| 4.6 | 3 |
| 4.8 | 1 |
| 5.0 | 3 |
| NA | 9 |
tttt <- ggplot(data = data, mapping = aes(x = wb_T3, fill = factor(wb_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(tttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T4の度数分布とヒストグラム
wb_T4_count <- dplyr::count(data, wb_T4)
knitr::kable(wb_T4_count) #テーブル化
| wb_T4 | n |
|---|---|
| 0.2 | 1 |
| 0.6 | 1 |
| 0.8 | 2 |
| 1.0 | 2 |
| 1.2 | 3 |
| 1.4 | 1 |
| 1.6 | 2 |
| 1.8 | 4 |
| 2.0 | 8 |
| 2.2 | 8 |
| 2.4 | 6 |
| 2.6 | 13 |
| 2.8 | 8 |
| 3.0 | 3 |
| 3.2 | 10 |
| 3.4 | 7 |
| 3.6 | 4 |
| 3.8 | 3 |
| 4.0 | 5 |
| 4.2 | 5 |
| 4.4 | 2 |
| 4.6 | 2 |
| 5.0 | 6 |
| NA | 8 |
uuuu <- ggplot(data = data, mapping = aes(x = wb_T4, fill = factor(wb_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(uuuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T1の度数分布とヒストグラム
ev_T1_count <- dplyr::count(data, ev_T1)
knitr::kable(ev_T1_count) #テーブル化
| ev_T1 | n |
|---|---|
| -3.0 | 3 |
| -2.5 | 1 |
| -2.0 | 1 |
| -1.5 | 7 |
| -1.0 | 4 |
| -0.5 | 9 |
| 0.0 | 21 |
| 0.5 | 18 |
| 1.0 | 11 |
| 1.5 | 1 |
| 2.0 | 3 |
| 2.5 | 15 |
| 3.0 | 19 |
| NA | 1 |
vvvv <- ggplot(data = data, mapping = aes(x = ev_T1, fill = factor(ev_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(vvvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T2の度数分布とヒストグラム
ev_T2_count <- dplyr::count(data, ev_T2)
knitr::kable(ev_T2_count) #テーブル化
| ev_T2 | n |
|---|---|
| -3.0 | 3 |
| -2.5 | 5 |
| -2.0 | 1 |
| -1.5 | 3 |
| -1.0 | 4 |
| -0.5 | 3 |
| 0.0 | 22 |
| 0.5 | 10 |
| 1.0 | 9 |
| 1.5 | 3 |
| 2.0 | 6 |
| 2.5 | 12 |
| 3.0 | 14 |
| NA | 19 |
wwww <- ggplot(data = data, mapping = aes(x = ev_T2, fill = factor(ev_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(wwww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T3の度数分布とヒストグラム
ev_T3_count <- dplyr::count(data, ev_T3)
knitr::kable(ev_T3_count) #テーブル化
| ev_T3 | n |
|---|---|
| -3.0 | 7 |
| -2.0 | 3 |
| -1.5 | 1 |
| -1.0 | 3 |
| -0.5 | 8 |
| 0.0 | 20 |
| 0.5 | 17 |
| 1.0 | 8 |
| 1.5 | 6 |
| 2.0 | 10 |
| 2.5 | 5 |
| 3.0 | 15 |
| NA | 11 |
panda <- ggplot(data = data, mapping = aes(x = ev_T3, fill = factor(ev_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(panda) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T4の度数分布とヒストグラム
ev_T4_count <- dplyr::count(data, ev_T4)
knitr::kable(ev_T4_count) #テーブル化
| ev_T4 | n |
|---|---|
| -3.0 | 3 |
| -2.0 | 1 |
| -1.0 | 3 |
| -0.5 | 10 |
| 0.0 | 21 |
| 0.5 | 15 |
| 1.0 | 9 |
| 1.5 | 8 |
| 2.0 | 10 |
| 2.5 | 7 |
| 3.0 | 14 |
| NA | 13 |
yyyy <- ggplot(data = data, mapping = aes(x = ev_T4, fill = factor(ev_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(yyyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_onemonthの度数分布とヒストグラム
hsc_onemonth_count <- dplyr::count(data, hsc_onemonth)
knitr::kable(hsc_onemonth_count) #テーブル化
| hsc_onemonth | n |
|---|---|
| 3.170833 | 1 |
| 3.533333 | 1 |
| 3.750000 | 1 |
| 3.887500 | 1 |
| 4.033333 | 1 |
| 4.100000 | 1 |
| 4.145833 | 1 |
| 4.262500 | 1 |
| 4.270833 | 1 |
| 4.279167 | 1 |
| 4.437500 | 1 |
| 4.441667 | 1 |
| 4.475000 | 1 |
| 4.504167 | 1 |
| 4.575000 | 2 |
| 4.600000 | 1 |
| 4.604167 | 1 |
| 4.645833 | 1 |
| 4.679167 | 1 |
| 4.750000 | 1 |
| 4.754167 | 1 |
| 4.762500 | 1 |
| 4.775000 | 1 |
| 4.804167 | 1 |
| 4.837500 | 1 |
| 4.883333 | 1 |
| 4.904167 | 1 |
| 4.912500 | 1 |
| 4.962500 | 1 |
| 4.966667 | 1 |
| 4.975000 | 1 |
| 5.008333 | 1 |
| 5.016667 | 1 |
| 5.037500 | 1 |
| 5.041667 | 1 |
| 5.058333 | 1 |
| 5.066667 | 1 |
| 5.079167 | 1 |
| 5.079167 | 1 |
| 5.116667 | 1 |
| 5.175000 | 1 |
| 5.200000 | 1 |
| 5.241667 | 1 |
| 5.270833 | 1 |
| 5.275000 | 1 |
| 5.287500 | 1 |
| 5.304167 | 2 |
| 5.329167 | 1 |
| 5.362500 | 1 |
| 5.383333 | 1 |
| 5.391667 | 1 |
| 5.400000 | 1 |
| 5.404167 | 1 |
| 5.420833 | 1 |
| 5.425000 | 1 |
| 5.533333 | 1 |
| 5.537500 | 1 |
| 5.554167 | 1 |
| 5.591667 | 1 |
| 5.608333 | 1 |
| 5.625000 | 1 |
| 5.637500 | 2 |
| 5.641667 | 1 |
| 5.650000 | 1 |
| 5.729167 | 1 |
| 5.737500 | 1 |
| 5.783333 | 1 |
| 5.795833 | 1 |
| 5.812500 | 1 |
| 5.829167 | 1 |
| 5.875000 | 1 |
| 5.879167 | 1 |
| 5.900000 | 1 |
| 5.987500 | 1 |
| 6.012500 | 1 |
| 6.029167 | 1 |
| 6.054167 | 1 |
| 6.095833 | 1 |
| 6.158333 | 1 |
| 6.237500 | 1 |
| 6.258333 | 1 |
| 6.570833 | 1 |
| 6.687500 | 1 |
| NA | 28 |
zzzz <- ggplot(data = data, mapping = aes(x = hsc_onemonth, fill = factor(hsc_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(zzzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_onemonthの度数分布とヒストグラム
wb_onemonth_count <- dplyr::count(data, wb_onemonth)
knitr::kable(wb_onemonth_count) #テーブル化
| wb_onemonth | n |
|---|---|
| 1.15 | 1 |
| 1.25 | 1 |
| 1.30 | 1 |
| 1.40 | 1 |
| 1.65 | 1 |
| 1.75 | 1 |
| 1.80 | 1 |
| 1.85 | 1 |
| 1.95 | 1 |
| 2.00 | 1 |
| 2.00 | 1 |
| 2.10 | 2 |
| 2.15 | 1 |
| 2.20 | 1 |
| 2.25 | 2 |
| 2.30 | 1 |
| 2.30 | 1 |
| 2.35 | 1 |
| 2.40 | 3 |
| 2.45 | 1 |
| 2.45 | 2 |
| 2.50 | 3 |
| 2.55 | 1 |
| 2.60 | 1 |
| 2.70 | 1 |
| 2.75 | 1 |
| 2.75 | 1 |
| 2.80 | 4 |
| 2.80 | 1 |
| 2.85 | 4 |
| 2.85 | 2 |
| 2.85 | 2 |
| 2.90 | 1 |
| 2.90 | 1 |
| 2.95 | 2 |
| 2.95 | 3 |
| 3.05 | 4 |
| 3.05 | 1 |
| 3.15 | 1 |
| 3.20 | 1 |
| 3.30 | 1 |
| 3.35 | 2 |
| 3.40 | 1 |
| 3.45 | 1 |
| 3.45 | 1 |
| 3.50 | 1 |
| 3.55 | 1 |
| 3.60 | 2 |
| 3.60 | 1 |
| 3.65 | 1 |
| 3.75 | 1 |
| 3.80 | 1 |
| 3.80 | 1 |
| 3.85 | 1 |
| 4.05 | 1 |
| 4.10 | 1 |
| 4.20 | 2 |
| 4.25 | 2 |
| 4.40 | 1 |
| 4.45 | 2 |
| 4.55 | 1 |
| 4.85 | 1 |
| NA | 26 |
A <- ggplot(data = data, mapping = aes(x = wb_onemonth, fill = factor(wb_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(A) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_onemonthの度数分布とヒストグラム
ev_onemonth_count <- dplyr::count(data, ev_onemonth)
knitr::kable(ev_onemonth_count) #テーブル化
| ev_onemonth | n |
|---|---|
| -1.625 | 1 |
| -1.125 | 1 |
| -0.875 | 1 |
| -0.750 | 2 |
| -0.625 | 1 |
| -0.500 | 1 |
| -0.375 | 1 |
| -0.250 | 3 |
| -0.125 | 2 |
| 0.000 | 4 |
| 0.125 | 6 |
| 0.250 | 7 |
| 0.375 | 2 |
| 0.500 | 4 |
| 0.625 | 2 |
| 0.750 | 6 |
| 0.875 | 3 |
| 1.000 | 2 |
| 1.125 | 2 |
| 1.250 | 3 |
| 1.375 | 5 |
| 1.500 | 5 |
| 1.625 | 3 |
| 1.750 | 4 |
| 1.875 | 3 |
| 2.125 | 2 |
| 2.250 | 2 |
| 2.500 | 1 |
| 2.625 | 2 |
| 2.750 | 1 |
| 2.875 | 2 |
| NA | 30 |
B <- ggplot(data = data, mapping = aes(x = ev_onemonth, fill = factor(ev_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(B) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T1
hsc_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T1.mean = mean (hsc1_T1), #hsc1_T1の平均
hsc1.T1.sd = sd (hsc1_T1), #hsc1_T1のSD
hsc2.T1.mean = mean (hsc2_T1),
hsc2.T1.sd = sd (hsc2_T1),
hsc3.T1.mean = mean (hsc3_T1),
hsc3.T1.sd = sd (hsc3_T1),
hsc4.T1.mean = mean (hsc4_T1),
hsc4.T1.sd = sd (hsc4_T1),
hsc5.T1.mean = mean (hsc5_T1),
hsc5.T1.sd = sd (hsc5_T1),
hsc6.T1.mean = mean (hsc6_T1),
hsc6.T1.sd = sd (hsc6_T1),
hsc7.T1.mean = mean (hsc7_T1),
hsc7.T1.sd = sd (hsc7_T1),
hsc8.T1.mean = mean (hsc8_T1),
hsc8.T1.sd = sd (hsc8_T1),
hsc9.T1.mean = mean (hsc9_T1),
hsc9.T1.sd = sd (hsc9_T1),
hsc10.T1.mean = mean (hsc10_T1),
hsc10.T1.sd = sd (hsc10_T1),
hsc11.T1.mean = mean (hsc11_T1),
hsc11.T1.sd = sd (hsc11_T1),
hsc12.T1.mean = mean (hsc4_T1),
hsc12.T1.sd = sd (hsc4_T1),
eoe.mean.T1 = mean (eoe_T1),
eoe.sd.T1 = sd (eoe_T1),
lst.mean.T1 = mean (lst_T1),
lst.sd.T1 = sd (lst_T1),
aes.mean.T1 = mean (aes_T1),
aes.sd.T1 = sd (aes_T1),
hsc.mean.T1 = mean (hsc_T1),
hsc.sd.T1 = sd (hsc_T1))
knitr::kable(hsc_T1_discriptive, digits = 2) #出力
| n | hsc1.T1.mean | hsc1.T1.sd | hsc2.T1.mean | hsc2.T1.sd | hsc3.T1.mean | hsc3.T1.sd | hsc4.T1.mean | hsc4.T1.sd | hsc5.T1.mean | hsc5.T1.sd | hsc6.T1.mean | hsc6.T1.sd | hsc7.T1.mean | hsc7.T1.sd | hsc8.T1.mean | hsc8.T1.sd | hsc9.T1.mean | hsc9.T1.sd | hsc10.T1.mean | hsc10.T1.sd | hsc11.T1.mean | hsc11.T1.sd | hsc12.T1.mean | hsc12.T1.sd | eoe.mean.T1 | eoe.sd.T1 | lst.mean.T1 | lst.sd.T1 | aes.mean.T1 | aes.sd.T1 | hsc.mean.T1 | hsc.sd.T1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 4.68 | 1.38 | 4.75 | 1.55 | 5.7 | 1.22 | 4.52 | 1.7 | 6.25 | 0.85 | 4.95 | 1.35 | 4.68 | 1.82 | 4.15 | 1.46 | 3.59 | 1.42 | 6.52 | 0.78 | 4.97 | 1.52 | 4.52 | 1.7 | 4.58 | 0.95 | 4.86 | 1.39 | 5.79 | 0.6 | 5.08 | 0.73 |
#hsc_T2
hsc_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T2.mean = mean (hsc1_T2), #hsc1_T2の平均
hsc1.T2.sd = sd (hsc1_T2), #hsc1_T2のSD
hsc2.T2.mean = mean (hsc2_T2),
hsc2.T2.sd = sd (hsc2_T2),
hsc3.T2.mean = mean (hsc3_T2),
hsc3.T2.sd = sd (hsc3_T2),
hsc4.T2.mean = mean (hsc4_T2),
hsc4.T2.sd = sd (hsc4_T2),
hsc5.T2.mean = mean (hsc5_T2),
hsc5.T2.sd = sd (hsc5_T2),
hsc6.T2.mean = mean (hsc6_T2),
hsc6.T2.sd = sd (hsc6_T2),
hsc7.T2.mean = mean (hsc7_T2),
hsc7.T2.sd = sd (hsc7_T2),
hsc8.T2.mean = mean (hsc8_T2),
hsc8.T2.sd = sd (hsc8_T2),
hsc9.T2.mean = mean (hsc9_T2),
hsc9.T2.sd = sd (hsc9_T2),
hsc10.T2.mean = mean (hsc10_T2),
hsc10.T2.sd = sd (hsc10_T2),
hsc11.T2.mean = mean (hsc11_T2),
hsc11.T2.sd = sd (hsc11_T2),
hsc12.T2.mean = mean (hsc4_T2),
hsc12.T2.sd = sd (hsc4_T2),
eoe.mean.T2 = mean (eoe_T2),
eoe.sd.T2 = sd (eoe_T2),
lst.mean.T2 = mean (lst_T2),
lst.sd.T2 = sd (lst_T2),
aes.mean.T2 = mean (aes_T2),
aes.sd.T2 = sd (aes_T2),
hsc.mean.T2 = mean (hsc_T2),
hsc.sd.T2 = sd (hsc_T2))
knitr::kable(hsc_T2_discriptive, digits = 2) #出力
| n | hsc1.T2.mean | hsc1.T2.sd | hsc2.T2.mean | hsc2.T2.sd | hsc3.T2.mean | hsc3.T2.sd | hsc4.T2.mean | hsc4.T2.sd | hsc5.T2.mean | hsc5.T2.sd | hsc6.T2.mean | hsc6.T2.sd | hsc7.T2.mean | hsc7.T2.sd | hsc8.T2.mean | hsc8.T2.sd | hsc9.T2.mean | hsc9.T2.sd | hsc10.T2.mean | hsc10.T2.sd | hsc11.T2.mean | hsc11.T2.sd | hsc12.T2.mean | hsc12.T2.sd | eoe.mean.T2 | eoe.sd.T2 | lst.mean.T2 | lst.sd.T2 | aes.mean.T2 | aes.sd.T2 | hsc.mean.T2 | hsc.sd.T2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 4.73 | 1.17 | 4.92 | 1.44 | 5.73 | 1.17 | 4.71 | 1.63 | 6.18 | 0.98 | 5.06 | 1.24 | 4.81 | 1.71 | 4.46 | 1.29 | 3.8 | 1.4 | 6.42 | 0.93 | 4.92 | 1.53 | 4.71 | 1.63 | 4.75 | 0.94 | 4.92 | 1.39 | 5.77 | 0.65 | 5.15 | 0.76 |
#hsc_T3
hsc_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T3.mean = mean (hsc1_T3), #hsc1_T3の平均
hsc1.T3.sd = sd (hsc1_T3), #hsc1_T3のSD
hsc2.T3.mean = mean (hsc2_T3),
hsc2.T3.sd = sd (hsc2_T3),
hsc3.T3.mean = mean (hsc3_T3),
hsc3.T3.sd = sd (hsc3_T3),
hsc4.T3.mean = mean (hsc4_T3),
hsc4.T3.sd = sd (hsc4_T3),
hsc5.T3.mean = mean (hsc5_T3),
hsc5.T3.sd = sd (hsc5_T3),
hsc6.T3.mean = mean (hsc6_T3),
hsc6.T3.sd = sd (hsc6_T3),
hsc7.T3.mean = mean (hsc7_T3),
hsc7.T3.sd = sd (hsc7_T3),
hsc8.T3.mean = mean (hsc8_T3),
hsc8.T3.sd = sd (hsc8_T3),
hsc9.T3.mean = mean (hsc9_T3),
hsc9.T3.sd = sd (hsc9_T3),
hsc10.T3.mean = mean (hsc10_T3),
hsc10.T3.sd = sd (hsc10_T3),
hsc11.T3.mean = mean (hsc11_T3),
hsc11.T3.sd = sd (hsc11_T3),
hsc12.T3.mean = mean (hsc4_T3),
hsc12.T3.sd = sd (hsc4_T3),
eoe.mean.T3 = mean (eoe_T3),
eoe.sd.T3 = sd (eoe_T3),
lst.mean.T3 = mean (lst_T3),
lst.sd.T3 = sd (lst_T3),
aes.mean.T3 = mean (aes_T3),
aes.sd.T3 = sd (aes_T3),
hsc.mean.T3 = mean (hsc_T3),
hsc.sd.T3 = sd (hsc_T3))
knitr::kable(hsc_T3_discriptive, digits = 2) #出力
| n | hsc1.T3.mean | hsc1.T3.sd | hsc2.T3.mean | hsc2.T3.sd | hsc3.T3.mean | hsc3.T3.sd | hsc4.T3.mean | hsc4.T3.sd | hsc5.T3.mean | hsc5.T3.sd | hsc6.T3.mean | hsc6.T3.sd | hsc7.T3.mean | hsc7.T3.sd | hsc8.T3.mean | hsc8.T3.sd | hsc9.T3.mean | hsc9.T3.sd | hsc10.T3.mean | hsc10.T3.sd | hsc11.T3.mean | hsc11.T3.sd | hsc12.T3.mean | hsc12.T3.sd | eoe.mean.T3 | eoe.sd.T3 | lst.mean.T3 | lst.sd.T3 | aes.mean.T3 | aes.sd.T3 | hsc.mean.T3 | hsc.sd.T3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 4.78 | 1.17 | 5.08 | 1.54 | 6.06 | 1.1 | 4.97 | 1.49 | 6.32 | 1.01 | 5.32 | 1.39 | 4.63 | 1.88 | 4.67 | 1.49 | 3.87 | 1.5 | 6.57 | 0.69 | 4.86 | 1.55 | 4.97 | 1.49 | 4.91 | 1.01 | 4.97 | 1.43 | 5.93 | 0.61 | 5.27 | 0.77 |
#hsc_T4
hsc_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T4.mean = mean (hsc1_T4), #hsc1_T4の平均
hsc1.T4.sd = sd (hsc1_T4), #hsc1_T4のSD
hsc2.T4.mean = mean (hsc2_T4),
hsc2.T4.sd = sd (hsc2_T4),
hsc3.T4.mean = mean (hsc3_T4),
hsc3.T4.sd = sd (hsc3_T4),
hsc4.T4.mean = mean (hsc4_T4),
hsc4.T4.sd = sd (hsc4_T4),
hsc5.T4.mean = mean (hsc5_T4),
hsc5.T4.sd = sd (hsc5_T4),
hsc6.T4.mean = mean (hsc6_T4),
hsc6.T4.sd = sd (hsc6_T4),
hsc7.T4.mean = mean (hsc7_T4),
hsc7.T4.sd = sd (hsc7_T4),
hsc8.T4.mean = mean (hsc8_T4),
hsc8.T4.sd = sd (hsc8_T4),
hsc9.T4.mean = mean (hsc9_T4),
hsc9.T4.sd = sd (hsc9_T4),
hsc10.T4.mean = mean (hsc10_T4),
hsc10.T4.sd = sd (hsc10_T4),
hsc11.T4.mean = mean (hsc11_T4),
hsc11.T4.sd = sd (hsc11_T4),
hsc12.T4.mean = mean (hsc4_T4),
hsc12.T4.sd = sd (hsc4_T4),
eoe.mean.T4 = mean (eoe_T4),
eoe.sd.T4 = sd (eoe_T4),
lst.mean.T4 = mean (lst_T4),
lst.sd.T4 = sd (lst_T4),
aes.mean.T4 = mean (aes_T4),
aes.sd.T4 = sd (aes_T4),
hsc.mean.T4 = mean (hsc_T4),
hsc.sd.T4 = sd (hsc_T4))
knitr::kable(hsc_T4_discriptive, digits = 2) #出力
| n | hsc1.T4.mean | hsc1.T4.sd | hsc2.T4.mean | hsc2.T4.sd | hsc3.T4.mean | hsc3.T4.sd | hsc4.T4.mean | hsc4.T4.sd | hsc5.T4.mean | hsc5.T4.sd | hsc6.T4.mean | hsc6.T4.sd | hsc7.T4.mean | hsc7.T4.sd | hsc8.T4.mean | hsc8.T4.sd | hsc9.T4.mean | hsc9.T4.sd | hsc10.T4.mean | hsc10.T4.sd | hsc11.T4.mean | hsc11.T4.sd | hsc12.T4.mean | hsc12.T4.sd | eoe.mean.T4 | eoe.sd.T4 | lst.mean.T4 | lst.sd.T4 | aes.mean.T4 | aes.sd.T4 | hsc.mean.T4 | hsc.sd.T4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 4.82 | 1.15 | 5.05 | 1.5 | 6.11 | 1.07 | 4.86 | 1.65 | 6.24 | 1.06 | 5.09 | 1.37 | 4.8 | 1.86 | 4.71 | 1.43 | 3.92 | 1.48 | 6.59 | 0.69 | 4.82 | 1.63 | 4.86 | 1.65 | 4.85 | 1.11 | 4.94 | 1.45 | 5.94 | 0.67 | 5.24 | 0.78 |
#hsc_onemonth
hsc_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.onemonth.mean = mean (hsc_onemonth), #hsc_onemonthの平均
hsc1.onemonth.sd = sd (hsc_onemonth)) #hsc_onemonthのSD
knitr::kable(hsc_onemonth_discriptive, digits = 2) #出力
| n | hsc1.onemonth.mean | hsc1.onemonth.sd |
|---|---|---|
| 79 | 5.18 | 0.68 |
#wb_T1
wb_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T1.mean = mean (wb1_T1), #hsc1_T10の平均
wb1.T1.sd = sd (wb1_T1), #hsc1_T10のSD
wb2.T1.mean = mean (wb2_T1),
wb2.T1.sd = sd (wb2_T1),
wb3.T1.mean = mean (wb3_T1),
wb3.T1.sd = sd (wb3_T1),
wb4.T1.mean = mean (wb4_T1),
wb4.T1.sd = sd (wb4_T1),
wb5.T1.mean = mean (wb5_T1),
wb5.T1.sd = sd (wb5_T1),
wb.T1.mean = mean (wb_T1),
wb.T1.sd = sd (wb_T1))
knitr::kable(wb_T1_discriptive, digits = 2) #出力
| n | wb1.T1.mean | wb1.T1.sd | wb2.T1.mean | wb2.T1.sd | wb3.T1.mean | wb3.T1.sd | wb4.T1.mean | wb4.T1.sd | wb5.T1.mean | wb5.T1.sd | wb.T1.mean | wb.T1.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 3.22 | 1 | 2.87 | 1.14 | 3.04 | 1.11 | 2.23 | 1.17 | 2.86 | 1.14 | 2.84 | 0.8 |
#wb_T2
wb_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T2.mean = mean (wb1_T2), #hsc1_T2の平均
wb1.T2.sd = sd (wb1_T2), #hsc1_T2のSD
wb2.T2.mean = mean (wb2_T2),
wb2.T2.sd = sd (wb2_T2),
wb3.T2.mean = mean (wb3_T2),
wb3.T2.sd = sd (wb3_T2),
wb4.T2.mean = mean (wb4_T2),
wb4.T2.sd = sd (wb4_T2),
wb5.T2.mean = mean (wb5_T2),
wb5.T2.sd = sd (wb5_T2),
wb.T2.mean = mean (wb_T2),
wb.T2.sd = sd (wb_T2))
knitr::kable(wb_T2_discriptive, digits = 2) #出力
| n | wb1.T2.mean | wb1.T2.sd | wb2.T2.mean | wb2.T2.sd | wb3.T2.mean | wb3.T2.sd | wb4.T2.mean | wb4.T2.sd | wb5.T2.mean | wb5.T2.sd | wb.T2.mean | wb.T2.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 3.22 | 1 | 3.13 | 1.11 | 3.15 | 1.17 | 2.38 | 1.29 | 2.94 | 1.24 | 2.96 | 0.93 |
#wb_T3
wb_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T3.mean = mean (wb1_T3), #hsc1_T3の平均
wb1.T3.sd = sd (wb1_T3), #hsc1_T3のSD
wb2.T3.mean = mean (wb2_T3),
wb2.T3.sd = sd (wb2_T3),
wb3.T3.mean = mean (wb3_T3),
wb3.T3.sd = sd (wb3_T3),
wb4.T3.mean = mean (wb4_T3),
wb4.T3.sd = sd (wb4_T3),
wb5.T3.mean = mean (wb5_T3),
wb5.T3.sd = sd (wb5_T3),
wb.T3.mean = mean (wb_T3),
wb.T3.sd = sd (wb_T3))
knitr::kable(wb_T3_discriptive, digits = 2) #出力
| n | wb1.T3.mean | wb1.T3.sd | wb2.T3.mean | wb2.T3.sd | wb3.T3.mean | wb3.T3.sd | wb4.T3.mean | wb4.T3.sd | wb5.T3.mean | wb5.T3.sd | wb.T3.mean | wb.T3.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 3.35 | 1.16 | 3.04 | 1.24 | 3.28 | 1.12 | 2.32 | 1.25 | 2.68 | 1.16 | 2.93 | 0.92 |
#wb_T4
wb_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T4.mean = mean (wb1_T4), #hsc1_T4の平均
wb1.T4.sd = sd (wb1_T4), #hsc1_T4のSD
wb2.T4.mean = mean (wb2_T4),
wb2.T4.sd = sd (wb2_T4),
wb3.T4.mean = mean (wb3_T4),
wb3.T4.sd = sd (wb3_T4),
wb4.T4.mean = mean (wb4_T4),
wb4.T4.sd = sd (wb4_T4),
wb5.T4.mean = mean (wb5_T4),
wb5.T4.sd = sd (wb5_T4),
wb.T4.mean = mean (wb_T4),
wb.T4.sd = sd (wb_T4))
knitr::kable(wb_T4_discriptive, digits = 2) #出力
| n | wb1.T4.mean | wb1.T4.sd | wb2.T4.mean | wb2.T4.sd | wb3.T4.mean | wb3.T4.sd | wb4.T4.mean | wb4.T4.sd | wb5.T4.mean | wb5.T4.sd | wb.T4.mean | wb.T4.sd |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 79 | 3.18 | 1.11 | 2.91 | 1.21 | 3.15 | 1.22 | 2.33 | 1.32 | 2.89 | 1.37 | 2.89 | 1.03 |
#wb_onemonth
wb_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb.onemonth.mean = mean (wb_onemonth), #wb_onemonthの平均
wb.onemonth.sd = sd (wb_onemonth)) #wb_onemonthのSD
knitr::kable(wb_onemonth_discriptive, digits = 2) #出力
| n | wb.onemonth.mean | wb.onemonth.sd |
|---|---|---|
| 79 | 2.91 | 0.81 |
#ev_T1
ev_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T1.mean = mean (ev1_T1), #ev1_T1の平均
ev1.T1.sd = sd (ev1_T1), #ev1_T1のSD
ev2.T1.mean = mean (ev2_T1),
ev2.T1.sd = sd (ev2_T1),
ev.T1.mean = mean (ev_T1),
ev.T1.sd = sd (ev_T1))
knitr::kable(ev_T1_discriptive, digits = 2) #出力
| n | ev1.T1.mean | ev1.T1.sd | ev2.T1.mean | ev2.T1.sd | ev.T1.mean | ev.T1.sd |
|---|---|---|---|---|---|---|
| 79 | 1.03 | 2.36 | 1.03 | 2.42 | 1.03 | 1.6 |
#ev_T2
ev_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T2.mean = mean (ev1_T2), #ev1_T2の平均
ev1.T2.sd = sd (ev1_T2), #ev1_T2のSD
ev2.T2.mean = mean (ev2_T2),
ev2.T2.sd = sd (ev2_T2),
ev.T2.mean = mean (ev_T2),
ev.T2.sd = sd (ev_T2))
knitr::kable(ev_T2_discriptive, digits = 2) #出力
| n | ev1.T2.mean | ev1.T2.sd | ev2.T2.mean | ev2.T2.sd | ev.T2.mean | ev.T2.sd |
|---|---|---|---|---|---|---|
| 79 | 1.29 | 2.27 | 0.2 | 2.48 | 0.75 | 1.62 |
#ev_T3
ev_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T3.mean = mean (ev_T3), #ev1_T3の平均
ev1.T3.sd = sd (ev1_T3), #ev1_T3のSD
ev2.T3.mean = mean (ev2_T3),
ev2.T3.sd = sd (ev2_T3),
ev.T3.mean = mean (ev_T3),
ev.T3.sd = sd (ev_T3))
knitr::kable(ev_T3_discriptive, digits = 2) #出力
| n | ev1.T3.mean | ev1.T3.sd | ev2.T3.mean | ev2.T3.sd | ev.T3.mean | ev.T3.sd |
|---|---|---|---|---|---|---|
| 79 | 0.73 | 2.38 | 0.53 | 2.43 | 0.73 | 1.58 |
#ev_T4
ev_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T4.mean = mean (ev1_T4), #ev1_T4の平均
ev1.T4.sd = sd (ev1_T4), #ev1_T4のSD
ev2.T4.mean = mean (ev2_T4),
ev2.T4.sd = sd (ev2_T4),
ev.T4.mean = mean (ev_T4),
ev.T4.sd = sd (ev_T4))
knitr::kable(ev_T4_discriptive, digits = 2) #出力
| n | ev1.T4.mean | ev1.T4.sd | ev2.T4.mean | ev2.T4.sd | ev.T4.mean | ev.T4.sd |
|---|---|---|---|---|---|---|
| 79 | 1.33 | 2.27 | 0.52 | 2.3 | 0.92 | 1.47 |
#ev_onemonth
ev_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev.onemonth.mean = mean (ev_onemonth), #ev_onemonthの平均
ev.onemonth.sd = sd (ev_onemonth)) #ev_onemonthのSD
knitr::kable(ev_onemonth_discriptive, digits = 2) #出力
| n | ev.onemonth.mean | ev.onemonth.sd |
|---|---|---|
| 79 | 0.86 | 0.98 |
#age_T1
age_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T1.mean = mean (age_T1), #age_T1の平均
age.T1.sd = sd (age_T1)) #age_T1のSD
knitr::kable(age_T1_discriptive, digits = 2) #出力
| n | age.T1.mean | age.T1.sd |
|---|---|---|
| 79 | 18.65 | 0.77 |
#age_T2
age_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T2.mean = mean (age_T2), #age_T2の平均
age.T2.sd = sd (age_T2)) #age_T2のSD
knitr::kable(age_T2_discriptive, digits = 2) #出力
| n | age.T2.mean | age.T2.sd |
|---|---|---|
| 79 | 18.65 | 0.77 |
#age_T3
age_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T3.mean = mean (age_T3), #age_T3の平均
age.T3.sd = sd (age_T3)) #age_T3のSD
knitr::kable(age_T3_discriptive, digits = 2) #出力
| n | age.T3.mean | age.T3.sd |
|---|---|---|
| 79 | 18.68 | 0.76 |
#age_T4
age_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T4.mean = mean (age_T4), #age_T4の平均
age.T4.sd = sd (age_T4)) #age_T4のSD
knitr::kable(age_T4_discriptive, digits = 2) #出力
| n | age.T4.mean | age.T4.sd |
|---|---|---|
| 79 | 18.68 | 0.76 |
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(GPArotation)
#hsc_T1
alpha(data[, c(6,7,8,9,11,12,13,14,15,16,17)]) #alpha .63
##
## Reliability analysis
## Call: alpha(x = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.63 0.69 0.14 1.7 0.048 4.9 0.68 0.13
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.64 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T1 0.66 0.65 0.69 0.16 1.9 0.047 0.024 0.129
## hsc2_T1 0.58 0.57 0.61 0.12 1.3 0.057 0.020 0.094
## hsc3_T1 0.64 0.64 0.69 0.15 1.8 0.048 0.028 0.140
## hsc4_T1 0.59 0.58 0.65 0.12 1.4 0.056 0.027 0.094
## hsc6_T1 0.59 0.58 0.63 0.12 1.4 0.056 0.021 0.105
## hsc7_T1 0.67 0.65 0.70 0.16 1.9 0.044 0.025 0.145
## hsc8_T1 0.56 0.55 0.61 0.11 1.2 0.059 0.019 0.094
## hsc9_T1 0.62 0.62 0.68 0.14 1.6 0.051 0.026 0.129
## hsc10_T1 0.64 0.65 0.69 0.15 1.8 0.049 0.026 0.140
## hsc11_T1 0.59 0.59 0.63 0.12 1.4 0.055 0.022 0.105
## hsc12_T1 0.62 0.61 0.67 0.14 1.6 0.051 0.025 0.105
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T1 114 0.27 0.28 0.142 0.087 4.8 1.38
## hsc2_T1 114 0.63 0.63 0.643 0.467 4.7 1.62
## hsc3_T1 113 0.31 0.34 0.193 0.139 5.6 1.31
## hsc4_T1 114 0.60 0.57 0.506 0.416 4.4 1.78
## hsc6_T1 114 0.58 0.59 0.561 0.437 4.9 1.41
## hsc7_T1 114 0.29 0.26 0.095 0.063 4.8 1.74
## hsc8_T1 114 0.70 0.68 0.700 0.581 4.1 1.38
## hsc9_T1 114 0.44 0.43 0.317 0.264 3.6 1.43
## hsc10_T1 114 0.21 0.30 0.152 0.111 6.5 0.77
## hsc11_T1 114 0.58 0.57 0.551 0.410 4.9 1.51
## hsc12_T1 114 0.46 0.46 0.360 0.286 5.6 1.43
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T1 0.02 0.07 0.09 0.14 0.42 0.17 0.10 0.00
## hsc2_T1 0.03 0.11 0.09 0.18 0.25 0.21 0.14 0.00
## hsc3_T1 0.00 0.04 0.04 0.11 0.20 0.31 0.31 0.01
## hsc4_T1 0.07 0.11 0.11 0.17 0.25 0.15 0.14 0.00
## hsc6_T1 0.02 0.04 0.11 0.18 0.31 0.22 0.12 0.00
## hsc7_T1 0.04 0.10 0.11 0.18 0.15 0.23 0.19 0.00
## hsc8_T1 0.03 0.14 0.11 0.37 0.21 0.11 0.04 0.00
## hsc9_T1 0.08 0.18 0.16 0.33 0.15 0.09 0.01 0.00
## hsc10_T1 0.00 0.00 0.00 0.03 0.09 0.25 0.63 0.00
## hsc11_T1 0.02 0.04 0.11 0.22 0.23 0.20 0.18 0.00
## hsc12_T1 0.02 0.04 0.04 0.02 0.25 0.32 0.31 0.00
omega(data[, c(6,7,8,9,11,12,13,14,15,16,17)],3,fm="ml") #omega hierarchical=.41, omega total=.74
## Omega
## Call: omega(m = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)],
## nfactors = 3, fm = "ml")
## Alpha: 0.63
## G.6: 0.69
## Omega Hierarchical: 0.41
## Omega H asymptotic: 0.56
## Omega Total 0.74
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T1 0.98 1.00 0.00 0.03
## hsc2_T1 0.66 0.69 0.91 0.09 0.48
## hsc3_T1 0.06 0.94 0.20
## hsc4_T1 0.30 0.32 0.22 0.78 0.42
## hsc6_T1 0.43 0.45 0.40 0.60 0.47
## hsc7_T1 0.03 0.97 0.00
## hsc8_T1 0.56 0.67 0.76 0.24 0.41
## hsc9_T1 0.23 0.33 0.17 0.83 0.31
## hsc10_T1 0.25 0.10 0.90 0.09
## hsc11_T1 0.48 0.44 0.46 0.54 0.50
## hsc12_T1 0.23 -0.21 0.15 0.85 0.36
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.41 0.98 0.76 1.09
##
## general/max 1.29 max/min = 1.43
## mean percent general = 0.3 with sd = 0.19 and cv of 0.63
## Explained Common Variance of the general factor = 0.33
##
## The degrees of freedom are 25 and the fit is 0.24
## The number of observations was 114 with Chi Square = 26.06 with prob < 0.4
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.031 and the 10 % confidence intervals are 0 0.079
## BIC = -92.35
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 0.99
## The number of observations was 114 with Chi Square = 106.32 with prob < 4.5e-07
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.116 and the 10 % confidence intervals are 0.085 0.139
## BIC = -102.07
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.75 0.75 0.75 0.99
## Multiple R square of scores with factors 0.57 0.57 0.56 0.98
## Minimum correlation of factor score estimates 0.13 0.14 0.13 0.97
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.74 0.61 0.69 0.45
## Omega general for total scores and subscales 0.41 0.24 0.31 0.10
## Omega group for total scores and subscales 0.26 0.37 0.38 0.35
#hsc_T2
alpha(data[, c(27,28,29,30,32,33,34,35,36,37,38)]) #alpha .78
##
## Reliability analysis
## Call: alpha(x = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.82 0.24 3.5 0.03 4.9 0.81 0.23
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.78 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T2 0.77 0.77 0.81 0.25 3.4 0.031 0.032 0.24
## hsc2_T2 0.75 0.75 0.79 0.23 3.0 0.035 0.027 0.20
## hsc3_T2 0.78 0.79 0.82 0.27 3.7 0.029 0.030 0.25
## hsc4_T2 0.75 0.76 0.80 0.24 3.1 0.033 0.031 0.20
## hsc6_T2 0.73 0.73 0.77 0.21 2.7 0.037 0.024 0.20
## hsc7_T2 0.79 0.79 0.83 0.27 3.7 0.028 0.030 0.26
## hsc8_T2 0.74 0.74 0.78 0.22 2.9 0.036 0.028 0.20
## hsc9_T2 0.77 0.77 0.82 0.25 3.4 0.031 0.029 0.23
## hsc10_T2 0.78 0.78 0.82 0.27 3.6 0.030 0.031 0.25
## hsc11_T2 0.75 0.75 0.79 0.23 3.0 0.034 0.029 0.20
## hsc12_T2 0.75 0.75 0.79 0.23 2.9 0.034 0.033 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T2 100 0.49 0.49 0.42 0.36 4.7 1.28
## hsc2_T2 100 0.67 0.66 0.65 0.56 4.8 1.48
## hsc3_T2 100 0.33 0.38 0.28 0.20 5.6 1.29
## hsc4_T2 100 0.63 0.61 0.57 0.49 4.5 1.74
## hsc6_T2 100 0.77 0.77 0.78 0.69 4.8 1.39
## hsc7_T2 100 0.36 0.34 0.22 0.18 4.8 1.71
## hsc8_T2 99 0.74 0.71 0.71 0.63 4.3 1.34
## hsc9_T2 100 0.50 0.48 0.40 0.35 3.8 1.51
## hsc10_T2 100 0.33 0.39 0.29 0.23 6.3 0.98
## hsc11_T2 99 0.66 0.64 0.62 0.54 4.7 1.58
## hsc12_T2 100 0.67 0.67 0.64 0.56 5.5 1.38
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T2 0.02 0.04 0.10 0.20 0.37 0.22 0.05 0.12
## hsc2_T2 0.01 0.08 0.12 0.13 0.30 0.24 0.12 0.12
## hsc3_T2 0.01 0.02 0.03 0.11 0.22 0.33 0.28 0.12
## hsc4_T2 0.07 0.11 0.10 0.11 0.27 0.25 0.09 0.12
## hsc6_T2 0.01 0.08 0.08 0.13 0.40 0.20 0.10 0.12
## hsc7_T2 0.03 0.09 0.09 0.23 0.10 0.27 0.19 0.12
## hsc8_T2 0.00 0.11 0.16 0.23 0.31 0.13 0.05 0.13
## hsc9_T2 0.04 0.20 0.15 0.30 0.17 0.09 0.05 0.12
## hsc10_T2 0.00 0.01 0.02 0.01 0.12 0.31 0.53 0.12
## hsc11_T2 0.02 0.10 0.11 0.14 0.30 0.18 0.14 0.13
## hsc12_T2 0.01 0.05 0.03 0.09 0.22 0.36 0.24 0.12
omega(data[, c(27,28,29,30,32,33,34,35,36,37,38)],3,fm="ml") #omega hierarchical=.64, omega total=.84
## Omega
## Call: omega(m = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)],
## nfactors = 3, fm = "ml")
## Alpha: 0.78
## G.6: 0.82
## Omega Hierarchical: 0.64
## Omega H asymptotic: 0.77
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T2 0.29 0.25 0.16 0.84 0.53
## hsc2_T2 0.57 0.72 0.84 0.16 0.38
## hsc3_T2 0.51 0.29 0.71 0.08
## hsc4_T2 0.52 0.21 0.32 0.68 0.86
## hsc6_T2 0.85 0.33 0.83 0.17 0.87
## hsc7_T2 0.06 0.94 0.36
## hsc8_T2 0.71 0.26 0.57 0.43 0.88
## hsc9_T2 0.48 0.22 -0.21 0.32 0.68 0.72
## hsc10_T2 0.66 0.47 0.53 0.06
## hsc11_T2 0.50 0.55 0.56 0.44 0.46
## hsc12_T2 0.56 0.30 0.44 0.56 0.72
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.77 0.29 0.89 0.89
##
## general/max 3.11 max/min = 3.03
## mean percent general = 0.54 with sd = 0.3 and cv of 0.56
## Explained Common Variance of the general factor = 0.57
##
## The degrees of freedom are 25 and the fit is 0.45
## The number of observations was 114 with Chi Square = 48.23 with prob < 0.0035
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.08
## RMSEA index = 0.096 and the 10 % confidence intervals are 0.051 0.129
## BIC = -70.17
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.16
## The number of observations was 114 with Chi Square = 124.99 with prob < 1.1e-09
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.132 and the 10 % confidence intervals are 0.102 0.154
## BIC = -83.4
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.40 0.84 0.77
## Multiple R square of scores with factors 0.79 0.16 0.71 0.59
## Minimum correlation of factor score estimates 0.59 -0.67 0.41 0.18
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.79 0.74 0.57
## Omega general for total scores and subscales 0.64 0.68 0.33 0.16
## Omega group for total scores and subscales 0.16 0.11 0.41 0.42
#hsc_T3
alpha(data[, c(48,49,50,51,53,54,55,56,57,58,59)]) #alpha .75
## Some items ( hsc3_T3 hsc10_T3 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
##
## Reliability analysis
## Call: alpha(x = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.73 0.79 0.2 2.8 0.033 5.1 0.72 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.68 0.75 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T3 0.74 0.73 0.79 0.21 2.6 0.033 0.058 0.25
## hsc2_T3 0.69 0.68 0.74 0.18 2.1 0.040 0.042 0.22
## hsc3_T3 0.77 0.76 0.80 0.24 3.2 0.030 0.045 0.25
## hsc4_T3 0.72 0.71 0.77 0.19 2.4 0.036 0.054 0.21
## hsc6_T3 0.69 0.67 0.73 0.17 2.0 0.041 0.042 0.21
## hsc7_T3 0.75 0.73 0.79 0.21 2.7 0.032 0.056 0.24
## hsc8_T3 0.69 0.67 0.74 0.17 2.1 0.042 0.038 0.21
## hsc9_T3 0.75 0.73 0.79 0.21 2.7 0.033 0.053 0.24
## hsc10_T3 0.76 0.76 0.79 0.24 3.1 0.031 0.044 0.25
## hsc11_T3 0.70 0.69 0.75 0.18 2.2 0.039 0.045 0.22
## hsc12_T3 0.73 0.71 0.76 0.19 2.4 0.035 0.056 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T3 105 0.42 0.45 0.342 0.288 4.8 1.22
## hsc2_T3 105 0.74 0.71 0.725 0.641 5.1 1.45
## hsc3_T3 105 0.11 0.19 0.054 -0.026 6.0 1.10
## hsc4_T3 105 0.59 0.57 0.509 0.455 5.0 1.45
## hsc6_T3 104 0.77 0.76 0.782 0.690 5.3 1.31
## hsc7_T3 105 0.49 0.43 0.312 0.284 4.8 1.82
## hsc8_T3 105 0.78 0.75 0.777 0.694 4.7 1.46
## hsc9_T3 105 0.45 0.42 0.304 0.280 3.9 1.49
## hsc10_T3 105 0.12 0.22 0.112 0.011 6.4 0.85
## hsc11_T3 105 0.70 0.67 0.672 0.577 4.9 1.50
## hsc12_T3 105 0.52 0.57 0.523 0.419 5.7 1.06
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T3 0.01 0.03 0.11 0.18 0.38 0.23 0.06 0.08
## hsc2_T3 0.02 0.05 0.09 0.10 0.27 0.33 0.14 0.08
## hsc3_T3 0.00 0.01 0.02 0.06 0.22 0.28 0.42 0.08
## hsc4_T3 0.03 0.07 0.03 0.16 0.35 0.23 0.13 0.08
## hsc6_T3 0.02 0.02 0.05 0.13 0.30 0.32 0.16 0.09
## hsc7_T3 0.05 0.10 0.08 0.21 0.12 0.22 0.22 0.08
## hsc8_T3 0.02 0.05 0.14 0.22 0.28 0.17 0.12 0.08
## hsc9_T3 0.06 0.14 0.15 0.28 0.24 0.10 0.04 0.08
## hsc10_T3 0.00 0.00 0.01 0.03 0.10 0.28 0.59 0.08
## hsc11_T3 0.03 0.04 0.10 0.21 0.24 0.24 0.15 0.08
## hsc12_T3 0.00 0.01 0.02 0.09 0.26 0.37 0.26 0.08
omega(data[, c(48,49,50,51,53,54,55,56,57,58,59)],3,fm="ml") #omega hierarchical=.59, omega total=.83
## Omega
## Call: omega(m = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)],
## nfactors = 3, fm = "ml")
## Alpha: 0.75
## G.6: 0.8
## Omega Hierarchical: 0.59
## Omega H asymptotic: 0.71
## Omega Total 0.83
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T3 0.28 0.11 0.89 0.68
## hsc2_T3 0.96 0.92 0.08 0.99
## hsc3_T3- -0.41 0.17 0.83 0.03
## hsc4_T3 0.32 0.42 0.29 0.71 0.36
## hsc6_T3 0.57 0.71 0.83 0.17 0.39
## hsc7_T3 0.27 0.10 0.90 0.70
## hsc8_T3 0.70 0.45 0.70 0.30 0.71
## hsc9_T3 0.32 0.15 0.85 0.67
## hsc10_T3- -0.99 1.00 0.00 0.01
## hsc11_T3 0.73 0.55 0.45 0.97
## hsc12_T3 0.24 0.44 0.28 0.35 0.65 0.16
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.70 1.19 0.01 1.29
##
## general/max 2.09 max/min = 162.31
## mean percent general = 0.52 with sd = 0.35 and cv of 0.68
## Explained Common Variance of the general factor = 0.52
##
## The degrees of freedom are 25 and the fit is 0.26
## The number of observations was 114 with Chi Square = 27.85 with prob < 0.31
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.04 and the 10 % confidence intervals are 0 0.084
## BIC = -90.55
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.38
## The number of observations was 114 with Chi Square = 148.56 with prob < 2.9e-13
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.15 and the 10 % confidence intervals are 0.12 0.171
## BIC = -59.83
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.97 0.88 0.08 1
## Multiple R square of scores with factors 0.93 0.78 0.01 1
## Minimum correlation of factor score estimates 0.86 0.56 -0.99 1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.83 0.79 0.92 0.71
## Omega general for total scores and subscales 0.59 0.49 0.92 0.01
## Omega group for total scores and subscales 0.26 0.29 0.00 0.70
#hsc_T4
alpha(data[, c(69,70,71,72,74,75,76,77,78,79,80)]) #alpha .79
##
## Reliability analysis
## Call: alpha(x = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.84 0.25 3.7 0.028 5 0.8 0.22
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.79 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T4 0.79 0.79 0.84 0.27 3.7 0.028 0.042 0.24
## hsc2_T4 0.77 0.76 0.81 0.24 3.2 0.031 0.036 0.22
## hsc3_T4 0.80 0.80 0.83 0.28 3.9 0.027 0.033 0.24
## hsc4_T4 0.76 0.76 0.81 0.24 3.1 0.032 0.040 0.19
## hsc6_T4 0.75 0.74 0.79 0.23 2.9 0.034 0.032 0.20
## hsc7_T4 0.79 0.78 0.83 0.26 3.6 0.028 0.042 0.22
## hsc8_T4 0.75 0.74 0.79 0.22 2.9 0.034 0.027 0.20
## hsc9_T4 0.78 0.78 0.83 0.26 3.5 0.029 0.042 0.20
## hsc10_T4 0.79 0.79 0.82 0.27 3.7 0.028 0.038 0.24
## hsc11_T4 0.77 0.76 0.81 0.24 3.2 0.031 0.035 0.21
## hsc12_T4 0.75 0.75 0.80 0.23 2.9 0.033 0.036 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T4 106 0.37 0.41 0.29 0.25 4.8 1.17
## hsc2_T4 106 0.61 0.60 0.57 0.49 5.0 1.44
## hsc3_T4 106 0.28 0.34 0.26 0.15 5.9 1.19
## hsc4_T4 106 0.66 0.65 0.61 0.54 4.8 1.58
## hsc6_T4 106 0.75 0.74 0.75 0.66 5.0 1.45
## hsc7_T4 106 0.50 0.47 0.37 0.33 4.9 1.73
## hsc8_T4 106 0.77 0.75 0.76 0.69 4.6 1.42
## hsc9_T4 106 0.50 0.48 0.39 0.36 3.9 1.45
## hsc10_T4 106 0.36 0.43 0.36 0.26 6.3 0.96
## hsc11_T4 106 0.64 0.62 0.60 0.52 4.8 1.57
## hsc12_T4 106 0.73 0.73 0.71 0.65 5.6 1.35
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T4 0.01 0.02 0.14 0.16 0.39 0.25 0.03 0.07
## hsc2_T4 0.00 0.08 0.10 0.08 0.30 0.30 0.12 0.07
## hsc3_T4 0.00 0.02 0.02 0.08 0.20 0.27 0.41 0.07
## hsc4_T4 0.05 0.05 0.12 0.11 0.33 0.21 0.13 0.07
## hsc6_T4 0.02 0.08 0.08 0.08 0.36 0.27 0.11 0.07
## hsc7_T4 0.04 0.08 0.10 0.15 0.17 0.25 0.20 0.07
## hsc8_T4 0.02 0.05 0.16 0.19 0.32 0.16 0.10 0.07
## hsc9_T4 0.06 0.12 0.18 0.28 0.23 0.10 0.03 0.07
## hsc10_T4 0.00 0.01 0.01 0.03 0.10 0.28 0.57 0.07
## hsc11_T4 0.02 0.09 0.10 0.17 0.26 0.21 0.14 0.07
## hsc12_T4 0.01 0.05 0.02 0.07 0.29 0.29 0.27 0.07
omega(data[, c(69,70,71,72,74,75,76,77,78,79,80)],3,fm="ml") #omega hierarchical=.71, omega total=.86
## Omega
## Call: omega(m = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)],
## nfactors = 3, fm = "ml")
## Alpha: 0.79
## G.6: 0.84
## Omega Hierarchical: 0.71
## Omega H asymptotic: 0.83
## Omega Total 0.86
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T4 0.22 0.24 0.10 0.90 0.46
## hsc2_T4 0.46 0.79 0.83 0.17 0.25
## hsc3_T4 0.74 0.55 0.45 0.01
## hsc4_T4 0.63 0.43 0.57 0.94
## hsc6_T4 0.83 0.70 0.30 0.99
## hsc7_T4 0.39 0.16 0.84 0.96
## hsc8_T4 0.84 0.23 0.80 0.20 0.89
## hsc9_T4 0.36 0.14 0.86 0.93
## hsc10_T4 0.77 0.61 0.39 0.05
## hsc11_T4 0.50 0.55 0.56 0.44 0.45
## hsc12_T4 0.74 0.56 0.44 0.98
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.17 0.01 1.01 1.24
##
## general/max 2.56 max/min = 95.33
## mean percent general = 0.63 with sd = 0.39 and cv of 0.62
## Explained Common Variance of the general factor = 0.58
##
## The degrees of freedom are 25 and the fit is 0.33
## The number of observations was 114 with Chi Square = 35.47 with prob < 0.08
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.067 and the 10 % confidence intervals are 0 0.104
## BIC = -82.93
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.41
## The number of observations was 114 with Chi Square = 152.05 with prob < 8.2e-14
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.152 and the 10 % confidence intervals are 0.122 0.173
## BIC = -56.35
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.94 0.07 0.88 0.87
## Multiple R square of scores with factors 0.88 0.01 0.78 0.75
## Minimum correlation of factor score estimates 0.76 -0.99 0.55 0.50
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.86 0.7 0.78 0.69
## Omega general for total scores and subscales 0.71 0.7 0.64 0.21
## Omega group for total scores and subscales 0.14 0.0 0.14 0.48
#wb_T1
alpha(data[, c(18:22)]) #alpha .78
##
## Reliability analysis
## Call: alpha(x = data[, c(18:22)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.77 0.42 3.6 0.033 2.7 0.83 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.78 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T1 0.71 0.71 0.68 0.38 2.5 0.044 0.012 0.36
## wb2_T1 0.72 0.73 0.69 0.40 2.7 0.042 0.020 0.39
## wb3_T1 0.72 0.72 0.70 0.39 2.6 0.044 0.019 0.38
## wb4_T1 0.78 0.78 0.75 0.48 3.6 0.033 0.012 0.50
## wb5_T1 0.75 0.75 0.73 0.43 3.0 0.039 0.018 0.44
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T1 114 0.77 0.78 0.74 0.63 3.1 1.1
## wb2_T1 114 0.75 0.76 0.70 0.59 2.8 1.1
## wb3_T1 113 0.77 0.77 0.70 0.61 2.9 1.2
## wb4_T1 114 0.64 0.63 0.49 0.42 2.1 1.2
## wb5_T1 114 0.71 0.71 0.60 0.52 2.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T1 0.01 0.05 0.24 0.34 0.27 0.09 0.00
## wb2_T1 0.02 0.11 0.27 0.35 0.20 0.05 0.00
## wb3_T1 0.02 0.10 0.23 0.32 0.25 0.09 0.01
## wb4_T1 0.04 0.31 0.30 0.24 0.08 0.04 0.00
## wb5_T1 0.03 0.13 0.25 0.33 0.19 0.06 0.00
omega(data[, c(18:22)],1,fm="ml") #omega hierarchical=.78, omega total=.79
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(18:22)], nfactors = 1, fm = "ml")
## Alpha: 0.78
## G.6: 0.77
## Omega Hierarchical: 0.78
## Omega H asymptotic: 0.99
## Omega Total 0.79
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T1 0.79 0.62 0.38 1
## wb2_T1 0.68 0.46 0.54 1
## wb3_T1 0.71 0.50 0.50 1
## wb4_T1 0.44 0.20 0.80 1
## wb5_T1 0.61 0.37 0.63 1
##
## With eigenvalues of:
## g F1*
## 2.1 0.0
##
## general/max 1.933559e+16 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.23
## The number of observations was 114 with Chi Square = 25.49 with prob < 0.00011
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.12
## RMSEA index = 0.194 and the 10 % confidence intervals are 0.121 0.267
## BIC = 1.8
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.23
## The number of observations was 114 with Chi Square = 25.49 with prob < 0.00011
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.194 and the 10 % confidence intervals are 0.121 0.267
## BIC = 1.8
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.90 0
## Multiple R square of scores with factors 0.81 0
## Minimum correlation of factor score estimates 0.63 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.79 0.78
## Omega general for total scores and subscales 0.78 0.78
## Omega group for total scores and subscales 0.00 0.00
#wb_T2
alpha(data[, c(39:43)]) #alpha .85
##
## Reliability analysis
## Call: alpha(x = data[, c(39:43)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.85 0.52 5.5 0.024 3 0.91 0.48
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.84 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T2 0.80 0.80 0.77 0.50 4.0 0.031 0.0099 0.46
## wb2_T2 0.79 0.79 0.75 0.49 3.8 0.032 0.0081 0.47
## wb3_T2 0.81 0.81 0.81 0.52 4.4 0.030 0.0204 0.47
## wb4_T2 0.83 0.83 0.82 0.56 5.0 0.026 0.0132 0.54
## wb5_T2 0.82 0.83 0.83 0.55 4.9 0.027 0.0168 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T2 100 0.81 0.82 0.80 0.70 3.2 1.0
## wb2_T2 99 0.83 0.84 0.82 0.73 3.1 1.1
## wb3_T2 100 0.79 0.79 0.72 0.66 3.1 1.1
## wb4_T2 100 0.75 0.73 0.65 0.58 2.4 1.3
## wb5_T2 100 0.75 0.75 0.65 0.59 3.0 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T2 0.00 0.05 0.23 0.30 0.33 0.09 0.12
## wb2_T2 0.00 0.07 0.23 0.28 0.32 0.09 0.13
## wb3_T2 0.00 0.10 0.16 0.36 0.25 0.13 0.12
## wb4_T2 0.02 0.26 0.29 0.23 0.12 0.08 0.12
## wb5_T2 0.00 0.12 0.26 0.28 0.21 0.13 0.12
omega(data[, c(39:43)],1,fm="ml") #omega hierarchical=.83, omega total=.85
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(39:43)], nfactors = 1, fm = "ml")
## Alpha: 0.85
## G.6: 0.85
## Omega Hierarchical: 0.83
## Omega H asymptotic: 0.98
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T2 0.84 0.71 0.29 1
## wb2_T2 0.86 0.74 0.26 1
## wb3_T2 0.65 0.43 0.57 1
## wb4_T2 0.63 0.40 0.60 1
## wb5_T2 0.60 0.36 0.64 1
##
## With eigenvalues of:
## g F1*
## 2.6 0.0
##
## general/max 4.745101e+16 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 47.58 with prob < 4.3e-09
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.13
## RMSEA index = 0.279 and the 10 % confidence intervals are 0.207 0.348
## BIC = 23.9
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 47.58 with prob < 4.3e-09
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.279 and the 10 % confidence intervals are 0.207 0.348
## BIC = 23.9
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.94 0
## Multiple R square of scores with factors 0.88 0
## Minimum correlation of factor score estimates 0.76 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.85 0.83
## Omega general for total scores and subscales 0.83 0.83
## Omega group for total scores and subscales 0.00 0.00
#wb_T3
alpha(data[, c(60:64)]) #alpha .85
##
## Reliability analysis
## Call: alpha(x = data[, c(60:64)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.85 0.54 5.8 0.022 2.9 0.94 0.51
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.85 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T3 0.81 0.81 0.78 0.52 4.4 0.029 0.0065 0.50
## wb2_T3 0.81 0.82 0.77 0.53 4.4 0.028 0.0029 0.51
## wb3_T3 0.83 0.83 0.81 0.55 4.8 0.026 0.0105 0.51
## wb4_T3 0.83 0.83 0.81 0.55 4.9 0.027 0.0109 0.53
## wb5_T3 0.83 0.83 0.81 0.55 4.9 0.026 0.0102 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T3 105 0.82 0.82 0.78 0.71 3.3 1.1
## wb2_T3 105 0.82 0.81 0.78 0.70 3.0 1.2
## wb3_T3 105 0.78 0.78 0.71 0.65 3.2 1.2
## wb4_T3 105 0.79 0.78 0.70 0.65 2.3 1.3
## wb5_T3 105 0.77 0.77 0.69 0.64 2.7 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T3 0.01 0.06 0.17 0.34 0.27 0.15 0.08
## wb2_T3 0.01 0.11 0.21 0.30 0.26 0.10 0.08
## wb3_T3 0.01 0.08 0.16 0.33 0.28 0.14 0.08
## wb4_T3 0.06 0.19 0.38 0.17 0.14 0.06 0.08
## wb5_T3 0.01 0.15 0.26 0.36 0.15 0.07 0.08
omega(data[, c(60:64)],1,fm="ml") #omega hierarchical=.85, omega total=.85
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(60:64)], nfactors = 1, fm = "ml")
## Alpha: 0.85
## G.6: 0.85
## Omega Hierarchical: 0.85
## Omega H asymptotic: 0.99
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T3 0.81 0.66 0.34 1
## wb2_T3 0.80 0.65 0.35 1
## wb3_T3 0.68 0.47 0.53 1
## wb4_T3 0.70 0.49 0.51 1
## wb5_T3 0.66 0.44 0.56 1
##
## With eigenvalues of:
## g F1*
## 2.7 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.3
## The number of observations was 114 with Chi Square = 32.82 with prob < 4.1e-06
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
## RMSEA index = 0.226 and the 10 % confidence intervals are 0.153 0.297
## BIC = 9.14
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.3
## The number of observations was 114 with Chi Square = 32.82 with prob < 4.1e-06
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.226 and the 10 % confidence intervals are 0.153 0.297
## BIC = 9.14
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.93 0
## Multiple R square of scores with factors 0.86 0
## Minimum correlation of factor score estimates 0.73 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.85 0.85
## Omega general for total scores and subscales 0.85 0.85
## Omega group for total scores and subscales 0.00 0.00
#wb_T4
alpha(data[, c(81:85)]) #alpha .90
##
## Reliability analysis
## Call: alpha(x = data[, c(81:85)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.63 8.7 0.016 2.9 1.1 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.9 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T4 0.86 0.86 0.86 0.61 6.3 0.021 0.0115 0.58
## wb2_T4 0.87 0.87 0.84 0.62 6.6 0.021 0.0053 0.63
## wb3_T4 0.88 0.88 0.87 0.65 7.5 0.018 0.0076 0.63
## wb4_T4 0.88 0.88 0.86 0.64 7.2 0.019 0.0051 0.68
## wb5_T4 0.87 0.88 0.87 0.64 7.1 0.020 0.0123 0.64
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T4 106 0.86 0.87 0.83 0.79 3.1 1.1
## wb2_T4 106 0.86 0.86 0.84 0.77 2.8 1.3
## wb3_T4 106 0.81 0.81 0.75 0.70 3.1 1.2
## wb4_T4 106 0.83 0.83 0.79 0.73 2.3 1.3
## wb5_T4 106 0.84 0.83 0.77 0.73 2.9 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T4 0.01 0.09 0.16 0.32 0.32 0.09 0.07
## wb2_T4 0.03 0.13 0.23 0.30 0.20 0.11 0.07
## wb3_T4 0.01 0.08 0.24 0.29 0.22 0.16 0.07
## wb4_T4 0.06 0.23 0.32 0.20 0.11 0.08 0.07
## wb5_T4 0.04 0.11 0.24 0.28 0.19 0.14 0.07
omega(data[, c(81:85)],1,fm="ml") #omega hierarchical=.90, omega total=.90
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(81:85)], nfactors = 1, fm = "ml")
## Alpha: 0.9
## G.6: 0.9
## Omega Hierarchical: 0.9
## Omega H asymptotic: 1
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T4 0.84 0.71 0.29 1
## wb2_T4 0.84 0.71 0.29 1
## wb3_T4 0.74 0.55 0.45 1
## wb4_T4 0.79 0.62 0.38 1
## wb5_T4 0.77 0.59 0.41 1
##
## With eigenvalues of:
## g F1*
## 3.2 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 46.84 with prob < 6.1e-09
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
## RMSEA index = 0.276 and the 10 % confidence intervals are 0.204 0.346
## BIC = 23.16
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 46.84 with prob < 6.1e-09
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.276 and the 10 % confidence intervals are 0.204 0.346
## BIC = 23.16
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.95 0
## Multiple R square of scores with factors 0.90 0
## Minimum correlation of factor score estimates 0.80 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.9 0.9
## Omega general for total scores and subscales 0.9 0.9
## Omega group for total scores and subscales 0.0 0.0
library(BaylorEdPsych)
missingdata <- data %>% dplyr::select(hsc_T1,hsc_T2,hsc_T3,hsc_T4,wb_T1,wb_T2,wb_T3,wb_T4,ev_T1,ev_T2,ev_T3,ev_T4,hsc_onemonth,wb_onemonth,ev_onemonth) #欠損値分析用のデータセット作成
LittleMCAR(missingdata)
## Loading required package: mvnmle
## this could take a while
## $chi.square
## [1] 450.7459
##
## $df
## [1] 190
##
## $p.value
## [1] 0
##
## $missing.patterns
## [1] 21
##
## $amount.missing
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1
## Number Missing 1.00000000 16.0000000 10.0000000 10.0000000 1.00000000
## Percent Missing 0.00877193 0.1403509 0.0877193 0.0877193 0.00877193
## wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## Number Missing 15.0000000 9.00000000 8.00000000 1.00000000 19.0000000
## Percent Missing 0.1315789 0.07894737 0.07017544 0.00877193 0.1666667
## ev_T3 ev_T4 hsc_onemonth wb_onemonth
## Number Missing 11.00000000 13.0000000 28.000000 26.0000000
## Percent Missing 0.09649123 0.1140351 0.245614 0.2280702
## ev_onemonth
## Number Missing 30.0000000
## Percent Missing 0.2631579
##
## $data
## $data$DataSet1
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1
## 2 5.416667 5.633333 5.416667 5.966667 3.4 3.8 3.0 3.6 0.5
## 4 5.616667 5.683333 5.533333 5.766667 4.0 3.4 3.4 4.4 2.0
## 5 4.750000 5.066667 5.033333 5.000000 2.2 3.8 2.2 2.6 0.0
## 6 5.316667 5.116667 5.800000 5.900000 2.8 3.4 2.4 2.8 1.0
## 7 6.083333 6.283333 6.916667 7.000000 2.0 1.4 1.0 0.8 3.0
## 8 5.666667 4.983333 5.133333 5.300000 3.0 2.2 2.4 2.4 0.5
## 10 6.300000 6.233333 6.133333 6.283333 1.6 2.0 2.6 4.0 0.0
## 11 5.516667 5.816667 6.366667 6.416667 1.6 2.4 2.6 2.6 -2.0
## 12 5.150000 5.183333 5.100000 4.883333 2.8 3.0 3.2 3.2 0.0
## 13 5.566667 5.100000 5.166667 5.316667 2.4 3.0 2.6 3.2 0.0
## 15 4.916667 5.383333 4.450000 4.250000 3.8 3.8 3.6 4.2 3.0
## 17 5.733333 6.016667 6.150000 6.483333 1.6 2.2 1.4 1.8 -0.5
## 21 5.500000 4.100000 4.966667 4.783333 3.6 3.6 3.8 3.4 0.0
## 22 4.650000 4.633333 4.950000 4.866667 1.8 1.6 1.8 2.0 -0.5
## 25 5.566667 3.066667 5.950000 3.833333 2.8 2.6 2.2 1.6 2.5
## 26 5.800000 6.016667 5.883333 6.250000 2.2 1.6 1.6 2.0 -0.5
## 27 5.466667 5.700000 5.300000 5.150000 4.0 4.0 3.8 3.2 0.0
## 28 4.450000 4.516667 4.916667 4.516667 1.8 3.0 3.0 2.2 1.0
## 32 6.383333 6.066667 6.133333 6.050000 2.2 2.2 2.2 2.4 0.5
## 34 5.233333 5.466667 5.333333 5.533333 0.6 1.8 1.8 2.4 -1.5
## 35 6.466667 5.616667 4.966667 3.916667 4.2 4.6 4.6 4.4 3.0
## 40 5.333333 5.233333 5.483333 5.483333 2.0 2.2 1.8 1.8 0.0
## 41 5.683333 4.733333 3.950000 4.650000 1.4 1.2 1.0 1.0 0.0
## 44 5.066667 4.900000 6.450000 5.033333 2.8 3.4 2.0 3.2 2.5
## 47 5.533333 6.066667 6.016667 5.900000 2.4 2.6 3.2 1.4 2.5
## 48 5.516667 6.150000 6.116667 5.716667 3.0 2.8 2.4 4.0 -0.5
## 49 5.583333 5.950000 6.083333 6.433333 3.4 3.2 4.0 1.2 2.5
## 52 4.383333 5.150000 4.483333 5.200000 3.0 2.6 2.8 2.6 2.5
## 53 4.866667 4.650000 5.183333 5.366667 3.0 2.0 1.8 2.8 -2.5
## 54 4.500000 5.450000 5.133333 5.383333 2.2 1.0 1.4 1.0 3.0
## 55 4.433333 4.166667 4.283333 4.233333 2.6 3.4 2.8 2.6 0.0
## 56 5.483333 5.616667 5.716667 5.733333 2.0 1.8 2.2 2.0 0.5
## 57 4.800000 5.300000 5.816667 5.766667 2.6 2.4 2.6 2.2 -0.5
## 60 4.116667 4.783333 4.566667 4.833333 4.0 3.8 3.2 4.2 -1.5
## 62 3.183333 2.916667 3.500000 3.083333 4.6 5.0 4.8 5.0 3.0
## 63 4.850000 4.883333 5.250000 5.250000 3.0 3.0 3.6 2.6 3.0
## 66 6.116667 5.666667 5.683333 5.850000 2.8 2.6 2.8 3.2 -3.0
## 68 4.350000 4.100000 4.016667 3.933333 2.8 2.6 2.8 3.0 0.5
## 69 4.766667 4.783333 4.933333 5.550000 2.0 1.8 2.4 1.8 -0.5
## 70 4.016667 4.316667 4.800000 4.616667 3.2 2.6 2.8 2.8 3.0
## 71 5.450000 5.433333 5.683333 5.983333 1.8 1.6 0.8 0.8 2.0
## 72 5.383333 5.683333 5.416667 5.883333 3.4 3.0 2.4 2.6 -0.5
## 73 5.166667 5.433333 5.333333 4.333333 2.6 2.2 3.2 3.2 0.0
## 74 4.516667 4.233333 4.533333 4.616667 3.0 3.2 2.8 2.8 3.0
## 75 4.183333 4.166667 4.700000 4.716667 2.2 3.0 2.4 2.0 0.5
## 77 4.850000 4.883333 5.166667 4.633333 3.6 2.2 2.8 3.6 -0.5
## 78 5.800000 5.533333 6.050000 5.750000 3.6 4.2 3.4 2.6 3.0
## 79 6.066667 5.800000 5.000000 4.350000 3.4 4.4 5.0 5.0 -1.5
## 80 5.800000 5.766667 5.983333 5.700000 3.0 2.8 3.0 2.4 1.0
## 81 5.000000 5.866667 4.866667 5.866667 3.0 4.0 3.8 3.2 2.0
## 82 4.500000 4.400000 3.383333 3.850000 3.2 1.6 3.4 3.2 1.0
## 83 3.383333 4.016667 4.083333 4.066667 3.0 3.2 3.2 2.8 0.5
## 84 4.683333 4.933333 4.983333 5.300000 2.4 2.6 3.0 1.8 2.5
## 85 4.483333 4.733333 4.566667 4.800000 2.2 1.8 2.2 3.2 -1.5
## 86 5.800000 5.700000 6.000000 5.683333 4.4 3.8 3.8 4.2 0.5
## 87 4.266667 5.233333 5.033333 5.116667 2.2 3.2 3.4 3.0 0.5
## 88 5.216667 5.183333 5.500000 5.416667 3.0 3.0 2.8 2.2 3.0
## 90 5.433333 5.783333 6.383333 5.350000 4.2 3.0 3.8 3.4 3.0
## 92 3.983333 4.516667 5.483333 6.183333 1.8 3.2 3.0 2.0 2.5
## 93 5.016667 5.000000 4.916667 4.933333 2.8 3.0 2.8 2.8 2.5
## 94 4.850000 4.866667 4.700000 4.633333 2.4 2.0 2.0 2.0 1.0
## 95 4.316667 6.683333 6.166667 5.750000 2.0 2.0 2.2 2.2 0.0
## 96 4.733333 4.466667 4.766667 4.750000 2.6 1.8 2.0 2.4 2.5
## 97 5.433333 5.883333 6.033333 6.250000 3.2 3.6 3.8 3.8 2.5
## 99 3.400000 3.616667 3.483333 3.633333 3.4 3.8 3.4 2.8 3.0
## 100 5.216667 5.016667 5.400000 5.466667 3.2 3.2 4.0 4.2 0.0
## 101 3.300000 3.850000 3.916667 3.933333 2.6 3.0 4.0 3.6 3.0
## 102 4.483333 4.933333 5.416667 5.316667 2.8 2.4 2.6 2.6 3.0
## 103 5.566667 5.516667 5.800000 5.333333 2.6 3.4 3.2 3.4 3.0
## 104 6.600000 6.800000 6.800000 6.550000 5.0 4.6 4.0 4.6 3.0
## 106 4.483333 4.433333 3.766667 4.366667 2.6 4.0 3.6 1.6 1.0
## 107 5.900000 6.233333 6.450000 6.450000 2.4 3.4 3.2 2.8 3.0
## 108 5.316667 5.333333 5.616667 5.433333 3.8 5.0 3.0 5.0 0.5
## 109 5.283333 4.550000 5.050000 5.816667 2.8 4.4 3.0 3.2 0.0
## 110 5.483333 5.350000 6.100000 5.633333 3.2 4.4 5.0 5.0 0.0
## 111 4.283333 4.033333 4.866667 4.833333 3.0 4.2 3.6 3.4 -1.0
## 112 5.166667 5.700000 5.733333 5.550000 3.4 3.0 3.0 2.2 2.5
## 113 5.200000 5.283333 4.983333 4.850000 3.8 3.0 5.0 5.0 1.0
## 114 4.883333 5.150000 5.233333 5.533333 3.8 4.4 4.6 4.2 3.0
## ev_T2 ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 2 0.5 0.5 2.5 5.608333 3.45 1.000
## 4 -1.0 0.0 2.0 5.650000 3.80 0.750
## 5 3.0 0.0 0.0 4.962500 2.70 0.750
## 6 0.5 3.0 1.0 5.533333 2.85 1.375
## 7 -2.5 -3.0 1.5 6.570833 1.30 -0.250
## 8 0.0 2.0 1.0 5.270833 2.50 0.875
## 10 0.5 0.5 0.0 6.237500 2.55 0.250
## 11 0.0 0.0 1.5 6.029167 2.30 -0.125
## 12 0.5 0.5 2.5 5.079167 3.05 0.875
## 13 2.5 1.0 3.0 5.287500 2.80 1.625
## 15 2.5 2.0 3.0 4.750000 3.85 2.625
## 17 -0.5 0.5 3.0 6.095833 1.75 0.625
## 21 -2.0 3.0 0.0 4.837500 3.60 0.250
## 22 0.0 0.0 1.0 4.775000 1.80 0.125
## 25 0.0 -0.5 -3.0 4.604167 2.30 -0.250
## 26 -1.0 0.5 0.5 5.987500 1.85 -0.125
## 27 0.0 -2.0 2.0 5.404167 3.75 0.000
## 28 0.0 -0.5 -0.5 4.600000 2.50 0.000
## 32 2.5 0.0 3.0 6.158333 2.25 1.500
## 34 -0.5 2.5 2.0 5.391667 1.65 0.625
## 35 3.0 0.5 0.5 5.241667 4.45 1.750
## 40 -2.5 1.0 2.0 5.383333 1.95 0.125
## 41 -2.5 -0.5 0.0 4.754167 1.15 -0.750
## 44 3.0 -3.0 3.0 5.362500 2.85 1.375
## 47 -0.5 0.0 3.0 5.879167 2.40 1.250
## 48 -3.0 -3.0 0.0 5.875000 3.05 -1.625
## 49 1.0 -1.5 -1.0 6.012500 2.95 0.250
## 52 1.0 3.0 1.0 4.804167 2.75 1.875
## 53 -1.0 0.0 1.5 5.016667 2.40 -0.500
## 54 -2.5 -2.0 -3.0 5.116667 1.40 -1.125
## 55 2.5 -3.0 -3.0 4.279167 2.85 -0.875
## 56 1.5 3.0 0.0 5.637500 2.00 1.250
## 57 0.0 -1.0 0.5 5.420833 2.45 -0.250
## 60 0.0 0.0 2.5 4.575000 3.80 0.250
## 62 0.0 0.0 0.0 3.170833 4.85 0.750
## 63 1.0 0.5 1.5 5.058333 3.05 1.500
## 66 2.5 1.5 -0.5 5.829167 2.85 0.125
## 68 2.0 2.5 1.5 4.100000 2.80 1.625
## 69 0.0 1.5 0.5 5.008333 2.00 0.375
## 70 3.0 0.5 2.0 4.437500 2.85 2.125
## 71 2.0 3.0 -2.0 5.637500 1.25 1.250
## 72 3.0 3.0 3.0 5.591667 2.85 2.125
## 73 0.0 0.5 -0.5 5.066667 2.80 0.000
## 74 2.0 1.0 3.0 4.475000 2.95 2.250
## 75 0.5 1.5 2.0 4.441667 2.40 1.125
## 77 -1.5 0.5 -1.0 4.883333 3.05 -0.625
## 78 3.0 2.0 2.5 5.783333 3.45 2.625
## 79 0.0 0.5 1.5 5.304167 4.45 0.125
## 80 1.5 -0.5 0.0 5.812500 2.80 0.500
## 81 2.0 2.0 0.0 5.400000 3.50 1.500
## 82 1.0 0.5 -0.5 4.033333 2.85 0.500
## 83 0.5 2.0 -1.0 3.887500 3.05 0.500
## 84 1.0 0.5 1.5 4.975000 2.45 1.375
## 85 0.5 3.0 1.0 4.645833 2.35 0.750
## 86 2.5 2.5 2.0 5.795833 4.05 1.875
## 87 -2.5 0.5 1.5 4.912500 2.95 0.000
## 88 3.0 1.0 0.0 5.329167 2.75 1.750
## 90 0.0 0.0 3.0 5.737500 3.60 1.500
## 92 0.0 -0.5 0.0 5.041667 2.50 0.500
## 93 2.5 1.5 0.5 4.966667 2.85 1.750
## 94 1.0 1.0 1.0 4.762500 2.10 1.000
## 95 -1.5 0.0 0.0 5.729167 2.10 -0.375
## 96 0.5 -1.0 1.0 4.679167 2.20 0.750
## 97 0.0 2.0 3.0 5.900000 3.60 1.875
## 99 1.0 3.0 -0.5 3.533333 3.35 1.625
## 100 2.0 3.0 0.5 5.275000 3.65 1.375
## 101 3.0 3.0 2.0 3.750000 3.30 2.750
## 102 2.5 1.0 2.5 5.037500 2.60 2.250
## 103 2.5 -2.0 0.0 5.554167 3.15 0.875
## 104 2.5 2.5 2.0 6.687500 4.55 2.500
## 106 0.0 0.0 0.0 4.262500 2.95 0.250
## 107 3.0 3.0 2.5 6.258333 2.95 2.875
## 108 0.0 0.5 0.0 5.425000 4.20 0.250
## 109 0.5 0.0 0.0 5.175000 3.35 0.125
## 110 3.0 1.5 1.0 5.641667 4.40 1.375
## 111 1.5 -0.5 0.5 4.504167 3.55 0.125
## 112 3.0 3.0 3.0 5.537500 2.90 2.875
## 113 0.0 2.0 0.0 5.079167 4.20 0.750
## 114 0.0 1.5 0.0 5.200000 4.25 1.125
##
## $data$DataSet2
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 89 NA 4.05 4.216667 4.533333 2.6 3.2 3.8 4 0.5 2.5
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 89 1 3 NA 3.4 1.75
##
## $data$DataSet3
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 67 4.766667 4.516667 NA 4.866667 2.6 1.6 1.8 2.6 0.5 2.5
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 67 -3 1 NA 2.15 0.25
##
## $data$DataSet4
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2 ev_T3
## 91 4.066667 NA 5.4 NA 3 5 4 5 2.5 0 0.5
## ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 91 3 NA 4.25 1.5
##
## $data$DataSet5
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 98 4.816667 4.95 5.666667 5.783333 NA 3 2.4 2.2 1 0
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 98 0 0.5 5.304167 NA 0.375
##
## $data$DataSet6
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 61 5.7 5.666667 5.733333 5.4 2.6 NA 2.4 2 0.5 -3
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 61 -0.5 0 5.625 NA -0.75
##
## $data$DataSet7
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 20 4.15 4.550000 4.150000 4.233333 4.2 4.0 4.4 3.8 3.0 NA
## 37 5.00 4.566667 4.400000 4.333333 3.2 3.2 2.6 3.8 2.5 NA
## 50 5.85 5.683333 6.566667 6.116667 1.4 2.6 3.8 3.4 2.5 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 20 3 2.5 4.270833 4.1 NA
## 37 2 2.0 4.575000 3.2 NA
## 50 2 0.5 6.054167 2.8 NA
##
## $data$DataSet8
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 31 6.133333 5.416667 4.883333 3.183333 3.6 2.4 2.4 0.6 0.5 1
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 31 0 NA 4.904167 2.25 NA
##
## $data$DataSet9
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 105 3.966667 4 4 4.616667 3 3 3 2.6 NA NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 105 NA NA 4.145833 2.9 NA
##
## $data$DataSet10
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 59 4.75 NA 4.433333 4.666667 2.2 2.4 2.6 2.6 0 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 59 -0.5 NA NA 2.45 NA
##
## $data$DataSet11
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 14 5.483333 NA 5.550000 5.783333 1.8 NA 2.8 2.2 0.0 NA
## 23 5.116667 NA 5.133333 5.166667 1.6 NA 3.0 3.4 -1.0 NA
## 29 5.833333 NA 5.633333 5.683333 2.0 NA 1.4 2.0 -3.0 NA
## 30 5.616667 NA 5.433333 5.566667 2.2 NA 3.2 3.6 -1.5 NA
## 39 4.850000 NA 5.100000 4.900000 3.8 NA 4.6 4.6 1.0 NA
## 51 5.333333 NA 4.766667 4.766667 2.2 NA 2.0 1.2 -1.5 NA
## 65 5.366667 NA 5.116667 5.100000 2.0 NA 2.2 2.6 1.5 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 14 0.5 0.5 NA NA NA
## 23 2.5 0.0 NA NA NA
## 29 -3.0 -0.5 NA NA NA
## 30 0.0 0.0 NA NA NA
## 39 3.0 0.5 NA NA NA
## 51 0.0 -0.5 NA NA NA
## 65 -1.0 -0.5 NA NA NA
##
## $data$DataSet12
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 1 5.733333 NA 6.216667 NA 1.4 NA 0 0.2 -3 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 1 -3 -0.5 NA NA NA
##
## $data$DataSet13
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 3 5.616667 5.250000 NA 4.783333 3.6 3.4 NA 3.0 0.5 0.5
## 18 4.933333 4.533333 NA 4.783333 1.2 1.8 NA 2.6 0.5 1.0
## 43 3.933333 3.716667 NA 3.583333 3.6 4.4 NA 4.0 0.0 3.0
## 64 3.650000 4.500000 NA 4.950000 2.8 2.4 NA 2.2 0.0 -1.0
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 3 NA 0.5 NA NA NA
## 18 NA -0.5 NA NA NA
## 43 NA 3.0 NA NA NA
## 64 NA 0.5 NA NA NA
##
## $data$DataSet14
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 38 5.05 NA 5.583333 5.116667 3.4 NA 3.8 3.4 1 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 38 NA 0.5 NA NA NA
##
## $data$DataSet15
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2 ev_T3
## 42 4.35 NA NA 4.916667 2.2 NA NA 1.2 -1.5 NA NA
## ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 42 0.5 NA NA NA
##
## $data$DataSet16
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 16 5.300000 5.200000 5.416667 NA 2.8 2.2 2.2 NA -0.5 -3
## 33 5.316667 5.200000 5.133333 NA 3.0 3.2 3.0 NA 1.0 2
## 76 4.850000 5.416667 5.316667 NA 1.4 3.2 2.4 NA -1.0 0
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 16 0 NA NA NA NA
## 33 0 NA NA NA NA
## 76 1 NA NA NA NA
##
## $data$DataSet17
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2 ev_T3
## 19 5.25 NA 5.7 5.766667 2.6 NA 3 2.4 0 NA 0
## ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 19 NA NA NA NA
##
## $data$DataSet18
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 24 5.766667 NA 5.516667 NA 1.4 NA 2.0 NA 2.5 NA
## 36 5.916667 NA 6.333333 NA 1.6 NA 3.8 NA -1.0 NA
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 24 2 NA NA NA NA
## 36 3 NA NA NA NA
##
## $data$DataSet19
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 9 3.416667 2.533333 NA 2.616667 1.8 3 NA 4 0.5 -1.5
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 9 NA NA NA NA NA
##
## $data$DataSet20
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## 46 3.766667 3.600000 NA NA 0.8 2.4 NA NA 0.0 3
## 58 5.283333 4.766667 NA NA 3.2 3.8 NA NA 0.5 3
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 46 NA NA NA NA NA
## 58 NA NA NA NA NA
##
## $data$DataSet21
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2 ev_T3
## 45 4.633333 NA NA NA 3.6 NA NA NA 0 NA NA
## ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## 45 NA NA NA NA
#irrパッケージ読み込み
library(irr)
## Loading required package: lpSolve
#ICCに必要な変数だけのデータセットを作成
icc_hsc <- data %>% dplyr::select("hsc_T1", "hsc_T2", "hsc_T3", "hsc_T4")
icc_wb <- data %>% dplyr::select("wb_T1", "wb_T2", "wb_T3", "wb_T4")
icc_ev <- data %>% dplyr::select("ev_T1", "ev_T2", "ev_T3", "ev_T4")
#ICC算出
icc(icc_hsc, "twoway", "agreement") #ICC = 0.70 [0.61 < ICC < 0.77]
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 86
## Raters = 4
## ICC(A,1) = 0.697
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(85,250) = 10.4 , p = 6.28e-48
##
## 95%-Confidence Interval for ICC Population Values:
## 0.612 < ICC < 0.774
icc(icc_wb, "twoway", "agreement") #ICC = 0.66 [0.57 < ICC < 0.74]
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 88
## Raters = 4
## ICC(A,1) = 0.66
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(87,264) = 8.76 , p = 4.52e-43
##
## 95%-Confidence Interval for ICC Population Values:
## 0.57 < ICC < 0.743
icc(icc_ev, "twoway", "agreement") #ICC = 0.18 [0.10 < ICC < 0.30]
## Single Score Intraclass Correlation
##
## Model: twoway
## Type : agreement
##
## Subjects = 84
## Raters = 4
## ICC(A,1) = 0.182
##
## F-Test, H0: r0 = 0 ; H1: r0 > 0
## F(83,251) = 1.89 , p = 8.71e-05
##
## 95%-Confidence Interval for ICC Population Values:
## 0.08 < ICC < 0.302
#HSC
cor.test(icc_hsc$hsc_T1, icc_hsc$hsc_T2)
##
## Pearson's product-moment correlation
##
## data: icc_hsc$hsc_T1 and icc_hsc$hsc_T2
## t = 10.904, df = 95, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6415053 0.8226747
## sample estimates:
## cor
## 0.7455598
cor.test(icc_hsc$hsc_T1, icc_hsc$hsc_T3)
##
## Pearson's product-moment correlation
##
## data: icc_hsc$hsc_T1 and icc_hsc$hsc_T3
## t = 9.4725, df = 101, p-value = 1.303e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5677280 0.7763658
## sample estimates:
## cor
## 0.685894
cor.test(icc_hsc$hsc_T1, icc_hsc$hsc_T4)
##
## Pearson's product-moment correlation
##
## data: icc_hsc$hsc_T1 and icc_hsc$hsc_T4
## t = 6.8905, df = 101, p-value = 4.863e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4176639 0.6841380
## sample estimates:
## cor
## 0.5654817
cor.test(icc_hsc$hsc_T2, icc_hsc$hsc_T3)
##
## Pearson's product-moment correlation
##
## data: icc_hsc$hsc_T2 and icc_hsc$hsc_T3
## t = 10.543, df = 88, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6388470 0.8263396
## sample estimates:
## cor
## 0.7470975
cor.test(icc_hsc$hsc_T3, icc_hsc$hsc_T4)
##
## Pearson's product-moment correlation
##
## data: icc_hsc$hsc_T3 and icc_hsc$hsc_T4
## t = 13.236, df = 95, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7217009 0.8656593
## sample estimates:
## cor
## 0.8052401
#WB
cor.test(icc_wb$wb_T1, icc_wb$wb_T2)
##
## Pearson's product-moment correlation
##
## data: icc_wb$wb_T1 and icc_wb$wb_T2
## t = 7.9501, df = 96, p-value = 3.623e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4933338 0.7364297
## sample estimates:
## cor
## 0.6300777
cor.test(icc_wb$wb_T1, icc_wb$wb_T3)
##
## Pearson's product-moment correlation
##
## data: icc_wb$wb_T1 and icc_wb$wb_T3
## t = 8.2783, df = 102, p-value = 5.073e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5027151 0.7365885
## sample estimates:
## cor
## 0.6339283
cor.test(icc_wb$wb_T1, icc_wb$wb_T4)
##
## Pearson's product-moment correlation
##
## data: icc_wb$wb_T1 and icc_wb$wb_T4
## t = 7.024, df = 103, p-value = 2.386e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4236354 0.6859388
## sample estimates:
## cor
## 0.5690925
cor.test(icc_wb$wb_T2, icc_wb$wb_T3)
##
## Pearson's product-moment correlation
##
## data: icc_wb$wb_T2 and icc_wb$wb_T3
## t = 10.81, df = 90, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6462816 0.8288334
## sample estimates:
## cor
## 0.7516088
cor.test(icc_wb$wb_T3, icc_wb$wb_T4)
##
## Pearson's product-moment correlation
##
## data: icc_wb$wb_T3 and icc_wb$wb_T4
## t = 11.151, df = 98, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6463498 0.8233120
## sample estimates:
## cor
## 0.7478276
#LE
cor.test(icc_ev$ev_T1, icc_ev$ev_T2)
##
## Pearson's product-moment correlation
##
## data: icc_ev$ev_T1 and icc_ev$ev_T2
## t = 2.5529, df = 93, p-value = 0.01231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05732192 0.43501490
## sample estimates:
## cor
## 0.2559085
cor.test(icc_ev$ev_T1, icc_ev$ev_T3)
##
## Pearson's product-moment correlation
##
## data: icc_ev$ev_T1 and icc_ev$ev_T3
## t = 1.5154, df = 101, p-value = 0.1328
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04573877 0.33302085
## sample estimates:
## cor
## 0.1491057
cor.test(icc_ev$ev_T1, icc_ev$ev_T4)
##
## Pearson's product-moment correlation
##
## data: icc_ev$ev_T1 and icc_ev$ev_T4
## t = 1.5295, df = 99, p-value = 0.1293
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04483482 0.33735747
## sample estimates:
## cor
## 0.1519358
cor.test(icc_ev$ev_T2, icc_ev$ev_T3)
##
## Pearson's product-moment correlation
##
## data: icc_ev$ev_T2 and icc_ev$ev_T3
## t = 2.824, df = 86, p-value = 0.005894
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08718673 0.47195651
## sample estimates:
## cor
## 0.2913095
cor.test(icc_ev$ev_T3, icc_ev$ev_T4)
##
## Pearson's product-moment correlation
##
## data: icc_ev$ev_T3 and icc_ev$ev_T4
## t = 2.5787, df = 93, p-value = 0.01149
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05989464 0.43710559
## sample estimates:
## cor
## 0.2583194
cordata <- data %>% dplyr::select(hsc_T1,hsc_T2,hsc_T3,hsc_T4,wb_T1,wb_T2,wb_T3,wb_T4,ev_T1,ev_T2,ev_T3,ev_T4,hsc_onemonth,wb_onemonth,ev_onemonth) %>% drop_na()
head(cordata)
names(cordata)
## [1] "hsc_T1" "hsc_T2" "hsc_T3" "hsc_T4"
## [5] "wb_T1" "wb_T2" "wb_T3" "wb_T4"
## [9] "ev_T1" "ev_T2" "ev_T3" "ev_T4"
## [13] "hsc_onemonth" "wb_onemonth" "ev_onemonth"
cormat <- round(cor(cordata),2)
head(cormat)
## hsc_T1 hsc_T2 hsc_T3 hsc_T4 wb_T1 wb_T2 wb_T3 wb_T4 ev_T1 ev_T2
## hsc_T1 1.00 0.71 0.70 0.59 0.04 -0.02 -0.06 0.06 -0.12 0.04
## hsc_T2 0.71 1.00 0.74 0.77 -0.03 -0.09 -0.06 0.02 -0.06 -0.04
## hsc_T3 0.70 0.74 1.00 0.81 -0.06 -0.06 -0.16 -0.07 0.02 -0.01
## hsc_T4 0.59 0.77 0.81 1.00 -0.22 -0.15 -0.26 -0.21 -0.08 -0.08
## wb_T1 0.04 -0.03 -0.06 -0.22 1.00 0.67 0.69 0.66 0.29 0.24
## wb_T2 -0.02 -0.09 -0.06 -0.15 0.67 1.00 0.75 0.66 0.20 0.34
## ev_T3 ev_T4 hsc_onemonth wb_onemonth ev_onemonth
## hsc_T1 -0.04 0.17 0.84 0.01 0.01
## hsc_T2 -0.04 0.29 0.91 -0.04 0.05
## hsc_T3 -0.17 0.22 0.91 -0.10 0.02
## hsc_T4 -0.03 0.25 0.90 -0.24 0.02
## wb_T1 0.12 0.13 -0.08 0.85 0.31
## wb_T2 0.09 0.13 -0.09 0.88 0.31
library(reshape2)
melted_cormat <- melt(cormat)
head(melted_cormat)
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) + coord_fixed()
library(pequod)
## Loading required package: car
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:psych':
##
## logit
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
data_c <- data %>% drop_na() %>% select_("hsc_T1", "hsc_T2", "hsc_T3", "hsc_T4", "wb_T1", "wb_T2", "wb_T3", "wb_T4", "ev_T1", "ev_T2", "ev_T3", "ev_T4", "hsc_onemonth", "wb_onemonth", "ev_onemonth") #na削除したうえで必要な変数抽出
data_c$hsc_T1_c <- data_c$hsc_T1 - mean(data_c$hsc_T1) #hsc_T1の中心化
data_c$hsc_T2_c <- data_c$hsc_T2 - mean(data_c$hsc_T2) #hsc_T2の中心化
data_c$hsc_T3_c <- data_c$hsc_T3 - mean(data_c$hsc_T3) #hsc_T3の中心化
data_c$hsc_T4_c <- data_c$hsc_T4 - mean(data_c$hsc_T4) #hsc_T4の中心化
data_c$ev_T1_c <- data_c$ev_T1 - mean(data_c$ev_T1) #ev_T1の中心化
data_c$ev_T2_c <- data_c$ev_T2 - mean(data_c$ev_T2) #ev_T2の中心化
data_c$ev_T3_c <- data_c$ev_T3 - mean(data_c$ev_T3) #ev_T3の中心化
data_c$ev_T4_c <- data_c$ev_T4 - mean(data_c$ev_T4) #ev_T4の中心化
data_c$hsc_onemonth_c <- data_c$hsc_onemonth - mean(data_c$hsc_onemonth) #hsc_onemonthの中心化
data_c$ev_onemonth_c <- data_c$ev_onemonth - mean(data_c$ev_onemonth) #ev_onemonthの中心化
#pequodで分析版
model_t1 <- lmres(wb_T1 ~ ev_T1 + hsc_T1 + ev_T1:hsc_T1, centered = c("wb_T1", "ev_T1", "hsc_T1"), data = data) #交互作用の検討
summary(model_t1)
## Formula:
## wb_T1 ~ ev_T1 + hsc_T1 + ev_T1.XX.hsc_T1
## <environment: 0x000000001984e6d8>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.3381 0.1143 0.0895 4.6048 3.0000 107 0.0045 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.7422 -0.5602 -0.0017 0.0000 0.5958 1.7839
##
## Coefficients
## Estimate StdErr t.value beta p.value
## (Intercept) -0.00098 0.07684 -0.01273 0.9899
## ev_T1 0.17819 0.04874 3.65578 0.3392 0.0004 ***
## hsc_T1 0.04394 0.10982 0.40014 0.0376 0.6898
## ev_T1.XX.hsc_T1 0.00611 0.06333 0.09655 0.0091 0.9233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## ev_T1 1.0401 0.9614
## hsc_T1 1.0676 0.9367
## ev_T1.XX.hsc_T1 1.0684 0.9360
#通常のlmで分析版(ステップ1:主効果モデル)
model_t1s1 <- lm(data_c$wb_T1 ~ data_c$ev_T1_c + data_c$hsc_T1_c)
summary(model_t1s1)
##
## Call:
## lm(formula = data_c$wb_T1 ~ data_c$ev_T1_c + data_c$hsc_T1_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.88468 -0.53601 -0.03658 0.48434 1.73469
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.84304 0.08714 32.627 < 2e-16 ***
## data_c$ev_T1_c 0.14728 0.05507 2.674 0.00917 **
## data_c$hsc_T1_c 0.08626 0.12166 0.709 0.48047
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7745 on 76 degrees of freedom
## Multiple R-squared: 0.08777, Adjusted R-squared: 0.06376
## F-statistic: 3.656 on 2 and 76 DF, p-value: 0.03048
AIC(model_t1s1)
## [1] 188.757
BIC(model_t1s1)
## [1] 198.2348
#通常のlmで分析版(ステップ2:交互作用モデル)
model_t1s2 <- lm(data_c$wb_T1 ~ data_c$ev_T1_c + data_c$hsc_T1_c + data_c$ev_T1_c:data_c$hsc_T1_c)
summary(model_t1s2)
##
## Call:
## lm(formula = data_c$wb_T1 ~ data_c$ev_T1_c + data_c$hsc_T1_c +
## data_c$ev_T1_c:data_c$hsc_T1_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.87631 -0.57551 -0.04676 0.51220 1.76186
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.84925 0.08800 32.377 <2e-16 ***
## data_c$ev_T1_c 0.14435 0.05547 2.602 0.0112 *
## data_c$hsc_T1_c 0.06433 0.12677 0.507 0.6133
## data_c$ev_T1_c:data_c$hsc_T1_c 0.04666 0.07234 0.645 0.5209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7775 on 75 degrees of freedom
## Multiple R-squared: 0.0928, Adjusted R-squared: 0.05651
## F-statistic: 2.557 on 3 and 75 DF, p-value: 0.06147
AIC(model_t1s2)
## [1] 190.3199
BIC(model_t1s2)
## [1] 202.1672
anova(model_t1s1, model_t1s2) #R^2の増加量の検定
#pequodで分析版
model_t2 <- lmres(wb_T2 ~ ev_T2 + hsc_T2 + ev_T2:hsc_T2, centered = c("wb_T2", "ev_T2", "hsc_T2"), data = data) #交互作用の検討
summary(model_t2)
## Formula:
## wb_T2 ~ ev_T2 + hsc_T2 + ev_T2.XX.hsc_T2
## <environment: 0x0000000019921d68>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.377 0.142 0.113 4.909 3.000 89 0.0033 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.5969 -0.5760 0.0040 0.0000 0.5055 2.2113
##
## Coefficients
## Estimate StdErr t.value beta p.value
## (Intercept) -0.02452 0.08855 -0.27691 0.78249
## ev_T2 0.16789 0.05420 3.09738 0.3073 0.00261 **
## hsc_T2 -0.04751 0.11207 -0.42387 -0.0419 0.67268
## ev_T2.XX.hsc_T2 0.11027 0.06492 1.69840 0.1691 0.09293 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## ev_T2 1.0211 0.9794
## hsc_T2 1.0133 0.9869
## ev_T2.XX.hsc_T2 1.0287 0.9721
#通常のlmで分析版(ステップ1:主効果モデル)
model_t2s1 <- lm(data_c$wb_T2 ~ data_c$ev_T2_c + data_c$hsc_T2_c)
summary(model_t2s1)
##
## Call:
## lm(formula = data_c$wb_T2 ~ data_c$ev_T2_c + data_c$hsc_T2_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5797 -0.6433 -0.0366 0.6547 2.2020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.96203 0.09914 29.877 < 2e-16 ***
## data_c$ev_T2_c 0.19566 0.06145 3.184 0.00211 **
## data_c$hsc_T2_c -0.09549 0.13098 -0.729 0.46823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8812 on 76 degrees of freedom
## Multiple R-squared: 0.125, Adjusted R-squared: 0.102
## F-statistic: 5.43 on 2 and 76 DF, p-value: 0.006248
AIC(model_t2s1)
## [1] 209.149
BIC(model_t2s1)
## [1] 218.6268
#通常のlmで分析版(ステップ2:交互作用モデル)
model_t2s2 <- lm(data_c$wb_T2 ~ data_c$ev_T2_c + data_c$hsc_T2_c + data_c$ev_T2_c:data_c$hsc_T2_c)
summary(model_t2s2)
##
## Call:
## lm(formula = data_c$wb_T2 ~ data_c$ev_T2_c + data_c$hsc_T2_c +
## data_c$ev_T2_c:data_c$hsc_T2_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.60908 -0.56768 -0.02116 0.66961 2.19084
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.96855 0.09814 30.250 < 2e-16 ***
## data_c$ev_T2_c 0.16817 0.06304 2.668 0.00935 **
## data_c$hsc_T2_c -0.07412 0.13020 -0.569 0.57086
## data_c$ev_T2_c:data_c$hsc_T2_c 0.14174 0.08637 1.641 0.10495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8715 on 75 degrees of freedom
## Multiple R-squared: 0.1554, Adjusted R-squared: 0.1216
## F-statistic: 4.599 on 3 and 75 DF, p-value: 0.005218
AIC(model_t2s2)
## [1] 208.3618
BIC(model_t2s2)
## [1] 220.209
anova(model_t2s1, model_t2s2) #R^2の増加量の検定
#### p<.10で交互作用が有意だったので単純傾斜検定
model_ss <- simpleSlope(model_t2, pred ="ev_T2", mod1 = "hsc_T2")
summary(model_ss) #High HSCだけ係数が有意 b = 0.26, p<.001
##
## ** Estimated points of wb_T2 **
##
## Low ev_T2 (-1 SD) High ev_T2 (+1 SD)
## Low hsc_T2 (-1 SD) -0.1170 0.1507
## High hsc_T2 (+1 SD) -0.4757 0.3747
##
##
##
## ** Simple Slopes analysis ( df= 89 ) **
##
## simple slope standard error t-value p.value
## Low hsc_T2 (-1 SD) 0.0808 0.0793 1.02 0.3110
## High hsc_T2 (+1 SD) 0.2568 0.0702 3.66 0.0004 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
##
## ** Bauer & Curran 95% CI **
##
## lower CI upper CI
## hsc_T2 -0.4007 10.456
PlotSlope(model_ss)
#### RoS testをRoismanのアプリで行うために共分散を算出
#reg <- lm(data_c$wb_T2_c ~ data_c$ev_T2_c + data_c$hsc_T2_c + #data_c$ev_T2_c:data_c$hsc_T2_c)
#summary(reg)
#round(hccm(reg, type = "hc0"), digits = 3)
#必要なパラメタのメモ
#* Intercept (b0) = -0.024 →切片
#* Variable X (b1) = 0.31 →環境変数(独立変数)の回帰係数
#* Variable Z (b2) = -0.04 →感受性変数(調整変数)の回帰係数
#* Interaction XZ (b3) = 0.17 →交互作用項の回帰係数
#* Variance parameter b1 = 0.05^2 = 0.003 →たぶんstand.errorの2乗のことだと思う
#* Variance parameter b2 = 0.11^2 = 0.012 →たぶんstand.errorの2乗のことだと思う
#* Variance parameter b3 = 0.06^2 = 0.004 →たぶんstand.errorの2乗のことだと思う
#* Covariance parameters b1 b3 = -0.001
#* Covariance parameters b2 b3 = -0.001
#* Degress of freedom (df) = 75 →アプリの説明によればN - 独立変数の数k - 1で計算される(この場合、79 - 3 - 1 = 75)
RoS on X testing
#pequodで分析版
model_t3 <- lmres(wb_T3 ~ ev_T3 + hsc_T3 + ev_T3:hsc_T3, centered = c("wb_T3", "ev_T3", "hsc_T3"), data = data) #交互作用の検討
summary(model_t3)
## Formula:
## wb_T3 ~ ev_T3 + hsc_T3 + ev_T3.XX.hsc_T3
## <environment: 0x00000000175b0a68>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.372 0.139 0.112 5.261 3.000 98 0.0021 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.5169 -0.5613 0.0126 0.0000 0.4938 2.0687
##
## Coefficients
## Estimate StdErr t.value beta p.value
## (Intercept) 0.01188 0.08902 0.13349 0.8941
## ev_T3 0.17000 0.05800 2.93108 0.2875 0.0042 **
## hsc_T3 -0.16275 0.12174 -1.33685 -0.1276 0.1844
## ev_T3.XX.hsc_T3 0.07700 0.06606 1.16561 0.1127 0.2466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## ev_T3 1.0951 0.9132
## hsc_T3 1.0361 0.9652
## ev_T3.XX.hsc_T3 1.0631 0.9407
#通常のlmで分析版(ステップ1:主効果モデル)
model_t3s1 <- lm(data_c$wb_T3 ~ data_c$ev_T3_c + data_c$hsc_T3_c)
summary(model_t3s1)
##
## Call:
## lm(formula = data_c$wb_T3 ~ data_c$ev_T3_c + data_c$hsc_T3_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2932 -0.5946 -0.0384 0.5409 2.1203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.93418 0.10217 28.718 <2e-16 ***
## data_c$ev_T3_c 0.09873 0.06592 1.498 0.138
## data_c$hsc_T3_c -0.15691 0.13561 -1.157 0.251
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9081 on 76 degrees of freedom
## Multiple R-squared: 0.05346, Adjusted R-squared: 0.02855
## F-statistic: 2.146 on 2 and 76 DF, p-value: 0.124
AIC(model_t3s1)
## [1] 213.9083
BIC(model_t3s1)
## [1] 223.386
#通常のlmで分析版(ステップ2:交互作用モデル)
model_t3s2 <- lm(data_c$wb_T3 ~ data_c$ev_T3_c + data_c$hsc_T3_c + data_c$ev_T3_c:data_c$hsc_T3_c)
summary(model_t3s2)
##
## Call:
## lm(formula = data_c$wb_T3 ~ data_c$ev_T3_c + data_c$hsc_T3_c +
## data_c$ev_T3_c:data_c$hsc_T3_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.33862 -0.58016 0.02516 0.52491 2.07513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.94674 0.10347 28.480 <2e-16 ***
## data_c$ev_T3_c 0.08734 0.06743 1.295 0.199
## data_c$hsc_T3_c -0.15430 0.13591 -1.135 0.260
## data_c$ev_T3_c:data_c$hsc_T3_c 0.06166 0.07365 0.837 0.405
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9099 on 75 degrees of freedom
## Multiple R-squared: 0.06222, Adjusted R-squared: 0.02471
## F-statistic: 1.659 on 3 and 75 DF, p-value: 0.1831
AIC(model_t3s2)
## [1] 215.1735
BIC(model_t3s2)
## [1] 227.0208
anova(model_t3s1, model_t3s2) #R^2の増加量の検定
#pequodで分析版
model_t4 <- lmres(wb_T4 ~ ev_T4 + hsc_T4 + ev_T4:hsc_T4, centered = c("wb_T4", "ev_T4", "hsc_T4"), data = data) #交互作用の検討
summary(model_t4)
## Formula:
## wb_T4 ~ ev_T4 + hsc_T4 + ev_T4.XX.hsc_T4
## <environment: 0x0000000017ec9420>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.357 0.128 0.100 4.634 3.000 95 0.0045 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.9067 -0.5602 -0.1458 0.0000 0.6003 2.3405
##
## Coefficients
## Estimate StdErr t.value beta p.value
## (Intercept) 0.05401 0.09761 0.55331 0.58135
## ev_T4 0.19110 0.07035 2.71637 0.2642 0.00784 **
## hsc_T4 -0.36233 0.13034 -2.77984 -0.2692 0.00656 **
## ev_T4.XX.hsc_T4 -0.05298 0.08312 -0.63738 -0.0614 0.52541
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## ev_T4 1.0298 0.9711
## hsc_T4 1.0214 0.9790
## ev_T4.XX.hsc_T4 1.0092 0.9909
#通常のlmで分析版(ステップ1:主効果モデル)
model_t4s1 <- lm(data_c$wb_T4 ~ data_c$ev_T4_c + data_c$hsc_T4_c)
summary(model_t4s1)
##
## Call:
## lm(formula = data_c$wb_T4 ~ data_c$ev_T4_c + data_c$hsc_T4_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9455 -0.5644 -0.1442 0.6794 2.3338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.89114 0.11180 25.859 <2e-16 ***
## data_c$ev_T4_c 0.16951 0.07914 2.142 0.0354 *
## data_c$hsc_T4_c -0.35649 0.14834 -2.403 0.0187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9937 on 76 degrees of freedom
## Multiple R-squared: 0.09866, Adjusted R-squared: 0.07494
## F-statistic: 4.16 on 2 and 76 DF, p-value: 0.01931
AIC(model_t4s1)
## [1] 228.139
BIC(model_t4s1)
## [1] 237.6168
#通常のlmで分析版(ステップ2:交互作用モデル)
model_t4s2 <- lm(data_c$wb_T4 ~ data_c$ev_T4_c + data_c$hsc_T4_c + data_c$ev_T4_c:data_c$hsc_T4_c)
summary(model_t4s2)
##
## Call:
## lm(formula = data_c$wb_T4 ~ data_c$ev_T4_c + data_c$hsc_T4_c +
## data_c$ev_T4_c:data_c$hsc_T4_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9441 -0.5746 -0.1419 0.6479 2.3135
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.90283 0.11553 25.126 <2e-16 ***
## data_c$ev_T4_c 0.16597 0.07997 2.075 0.0414 *
## data_c$hsc_T4_c -0.36725 0.15115 -2.430 0.0175 *
## data_c$ev_T4_c:data_c$hsc_T4_c -0.04147 0.09472 -0.438 0.6628
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.999 on 75 degrees of freedom
## Multiple R-squared: 0.101, Adjusted R-squared: 0.065
## F-statistic: 2.807 on 3 and 75 DF, p-value: 0.0453
AIC(model_t4s2)
## [1] 229.9373
BIC(model_t4s2)
## [1] 241.7845
anova(model_t4s1, model_t4s2) #R^2の増加量の検定
#pequodで分析版
model_onemonth <- lmres(wb_onemonth ~ ev_onemonth + hsc_onemonth + ev_onemonth:hsc_onemonth, centered = c("wb_onemonth", "ev_onemonth", "hsc_onemonth"), data = data) #交互作用の検討
summary(model_onemonth)
## Formula:
## wb_onemonth ~ ev_onemonth + hsc_onemonth + ev_onemonth.XX.hsc_onemonth
## <environment: 0x0000000015c1f058>
##
## Models
## R R^2 Adj. R^2 F df1 df2 p.value
## Model 0.3610 0.1303 0.0955 3.7454 3.0000 75 0.015 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residuals
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.7044 -0.5392 -0.0975 0.0000 0.5028 1.7618
##
## Coefficients
## Estimate StdErr t.value beta p.value
## (Intercept) -0.01883 0.08661 -0.21743 0.82846
## ev_onemonth 0.24595 0.08966 2.74295 0.2978 0.00761 **
## hsc_onemonth -0.17001 0.13083 -1.29944 -0.1421 0.19777
## ev_onemonth.XX.hsc_onemonth 0.15957 0.12352 1.29192 0.1423 0.20035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Collinearity
## VIF Tolerance
## ev_onemonth 1.0161 0.9841
## hsc_onemonth 1.0309 0.9701
## ev_onemonth.XX.hsc_onemonth 1.0465 0.9555
#通常のlmで分析版(ステップ1:主効果モデル)
model_1ms1 <- lm(data_c$wb_onemonth ~ data_c$ev_onemonth_c + data_c$hsc_onemonth_c)
summary(model_1ms1)
##
## Call:
## lm(formula = data_c$wb_onemonth ~ data_c$ev_onemonth_c + data_c$hsc_onemonth_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6956 -0.4876 -0.1428 0.5073 1.7500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.90759 0.08694 33.445 < 2e-16 ***
## data_c$ev_onemonth_c 0.26015 0.08938 2.911 0.00473 **
## data_c$hsc_onemonth_c -0.14117 0.12948 -1.090 0.27902
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7727 on 76 degrees of freedom
## Multiple R-squared: 0.1109, Adjusted R-squared: 0.08755
## F-statistic: 4.742 on 2 and 76 DF, p-value: 0.01146
AIC(model_1ms1)
## [1] 188.393
BIC(model_1ms1)
## [1] 197.8708
#通常のlmで分析版(ステップ2:交互作用モデル)
model_1ms2 <- lm(data_c$wb_onemonth ~ data_c$ev_onemonth_c + data_c$hsc_onemonth_c + data_c$ev_onemonth_c:data_c$hsc_onemonth_c)
summary(model_1ms2)
##
## Call:
## lm(formula = data_c$wb_onemonth ~ data_c$ev_onemonth_c + data_c$hsc_onemonth_c +
## data_c$ev_onemonth_c:data_c$hsc_onemonth_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.70436 -0.53917 -0.09754 0.50279 1.76185
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 2.90456 0.08659 33.544
## data_c$ev_onemonth_c 0.24856 0.08944 2.779
## data_c$hsc_onemonth_c -0.16780 0.13055 -1.285
## data_c$ev_onemonth_c:data_c$hsc_onemonth_c 0.15957 0.12352 1.292
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## data_c$ev_onemonth_c 0.00689 **
## data_c$hsc_onemonth_c 0.20264
## data_c$ev_onemonth_c:data_c$hsc_onemonth_c 0.20035
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7693 on 75 degrees of freedom
## Multiple R-squared: 0.1303, Adjusted R-squared: 0.09551
## F-statistic: 3.745 on 3 and 75 DF, p-value: 0.0145
AIC(model_1ms2)
## [1] 188.6542
BIC(model_1ms2)
## [1] 200.5014
anova(model_1ms1, model_1ms2) #R^2の増加量の検定
library(soilphysics) #非線形モデルで準R2を算出するため使用
## Loading required package: rpanel
## Loading required package: tcltk
## Package `rpanel', version 1.1-4: type help(rpanel) for summary information
##
## Attaching package: 'rpanel'
## The following object is masked from 'package:tidyr':
##
## population
## Loading required package: MASS
##
## Attaching package: 'MASS'
## The following object is masked from 'package:plotly':
##
## select
## The following object is masked from 'package:dplyr':
##
## select
## Loading required package: tkrplot
## ---
## soilphysics version 3.1
##
weak_diff_T1 <- nls(wb_T1 ~ B0 + B1*(ev_T1 - C) + B3*((ev_T1 - C)*hsc_T1),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T1)
##
## Formula: wb_T1 ~ B0 + B1 * (ev_T1 - C) + B3 * ((ev_T1 - C) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 1.452774 14.392736 0.101 0.920
## B1 0.147319 0.330826 0.445 0.657
## C -6.395367 80.344866 -0.080 0.937
## B3 0.006115 0.063327 0.097 0.923
##
## Residual standard error: 0.8036 on 107 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 8.45e-06
## (3 observations deleted due to missingness)
AIC(weak_diff_T1)
## [1] 272.4009
BIC(weak_diff_T1)
## [1] 285.9485
pred <- predict(weak_diff_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T1)
w <- weights(weak_diff_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1143433
##
## $adj.R.squared
## [1] 0.0895118
strong_diff_T1 <- nls(wb_T1 ~ B0 + 0*(ev_T1 - C) + B3*((ev_T1 - C)*hsc_T1),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T1)
##
## Formula: wb_T1 ~ B0 + 0 * (ev_T1 - C) + B3 * ((ev_T1 - C) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.574113 0.543269 4.738 6.6e-06 ***
## C -0.159786 3.113120 -0.051 0.959160
## B3 0.034014 0.009184 3.704 0.000337 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8007 on 108 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 3.201e-06
## (3 observations deleted due to missingness)
AIC(strong_diff_T1)
## [1] 270.6064
BIC(strong_diff_T1)
## [1] 281.4445
pred <- predict(strong_diff_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T1)
w <- weights(strong_diff_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1127019
##
## $adj.R.squared
## [1] 0.09627048
weak_diathesis_T1 <- nls(wb_T1 ~ B0 + B1*(ev_T1 + 3) + B3*((ev_T1 + 3)*hsc_T1), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T1)
##
## Formula: wb_T1 ~ B0 + B1 * (ev_T1 + 3) + B3 * ((ev_T1 + 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.06101 0.19591 10.520 <2e-16 ***
## B1 0.12509 0.12615 0.992 0.324
## B3 0.01038 0.02366 0.439 0.662
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7999 on 108 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 4.062e-08
## (3 observations deleted due to missingness)
AIC(weak_diathesis_T1)
## [1] 270.4063
BIC(weak_diathesis_T1)
## [1] 281.2445
pred <- predict(weak_diathesis_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T1)
w <- weights(weak_diathesis_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1142995
##
## $adj.R.squared
## [1] 0.09789763
strong_diathesis_T1 <- nls(wb_T1 ~ B0 + 0*(ev_T1 + 3) + B3*((ev_T1 + 3)*hsc_T1),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T1)
##
## Formula: wb_T1 ~ B0 + 0 * (ev_T1 + 3) + B3 * ((ev_T1 + 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.121405 0.186187 11.394 < 2e-16 ***
## B3 0.032112 0.008921 3.599 0.000481 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7999 on 109 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 4.515e-08
## (3 observations deleted due to missingness)
AIC(strong_diathesis_T1)
## [1] 269.4123
BIC(strong_diathesis_T1)
## [1] 277.5409
pred <- predict(strong_diathesis_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T1)
w <- weights(strong_diathesis_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.106236
##
## $adj.R.squared
## [1] 0.09803635
weak_vs_T1 <- nls(wb_T1 ~ B0 + B1*(ev_T1 - 3) + B3*((ev_T1 - 3)*hsc_T1), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T1)
##
## Formula: wb_T1 ~ B0 + B1 * (ev_T1 - 3) + B3 * ((ev_T1 - 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.12607 0.13037 23.978 <2e-16 ***
## B1 0.23339 0.23091 1.011 0.314
## B3 -0.01082 0.04288 -0.252 0.801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8004 on 108 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 8.635e-08
## (3 observations deleted due to missingness)
AIC(weak_vs_T1)
## [1] 270.5387
BIC(weak_vs_T1)
## [1] 281.3769
pred <- predict(weak_vs_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T1)
w <- weights(weak_vs_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1132424
##
## $adj.R.squared
## [1] 0.09682095
strong_vs_T1 <- nls(wb_T1 ~ B0 + 0*(ev_T1 - 3) + B3*((ev_T1 - 3)*hsc_T1),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T1)
##
## Formula: wb_T1 ~ B0 + 0 * (ev_T1 - 3) + B3 * ((ev_T1 - 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.090684 0.125596 24.608 < 2e-16 ***
## B3 0.031592 0.008841 3.573 0.000527 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8005 on 109 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 8.133e-08
## (3 observations deleted due to missingness)
AIC(strong_vs_T1)
## [1] 269.5838
BIC(strong_vs_T1)
## [1] 277.7124
pred <- predict(strong_vs_T1) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T1)
w <- weights(strong_vs_T1)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T1)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1048542
##
## $adj.R.squared
## [1] 0.09664187
weak_diff_T2 <- nls(wb_T2 ~ B0 + B1*(ev_T2 - C) + B3*((ev_T2 - C)*hsc_T2),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T2 - C) + B3 * ((ev_T2 - C) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.01952 0.20052 15.058 <2e-16 ***
## B1 -0.38863 0.33905 -1.146 0.2548
## C 1.15712 1.07134 1.080 0.2830
## B3 0.11027 0.06492 1.698 0.0929 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.852 on 89 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 2.625e-06
## (21 observations deleted due to missingness)
AIC(weak_diff_T2)
## [1] 240.0366
BIC(weak_diff_T2)
## [1] 252.6996
pred <- predict(weak_diff_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T2)
w <- weights(weak_diff_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1419812
##
## $adj.R.squared
## [1] 0.1130592
strong_diff_T2 <- nls(wb_T2 ~ B0 + 0*(ev_T2 - C) + B3*((ev_T2 - C)*hsc_T2),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T2 - C) + B3 * ((ev_T2 - C) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.23686 0.57242 5.655 1.82e-07 ***
## C 2.32133 3.10572 0.747 0.456748
## B3 0.03679 0.01031 3.569 0.000577 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8535 on 90 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 1.361e-07
## (21 observations deleted due to missingness)
AIC(strong_diff_T2)
## [1] 239.3994
BIC(strong_diff_T2)
## [1] 249.5298
pred <- predict(strong_diff_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T2)
w <- weights(strong_diff_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1293146
##
## $adj.R.squared
## [1] 0.109966
weak_diathesis_T2 <- nls(wb_T2 ~ B0 + B1*(ev_T2 + 3) + B3*((ev_T2 + 3)*hsc_T2), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T2 + 3) + B3 * ((ev_T2 + 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.260343 0.223576 10.110 <2e-16 ***
## B1 0.152608 0.154264 0.989 0.325
## B3 0.005785 0.028574 0.202 0.840
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8623 on 90 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.384e-07
## (21 observations deleted due to missingness)
AIC(weak_diathesis_T2)
## [1] 241.3154
BIC(weak_diathesis_T2)
## [1] 251.4458
pred <- predict(weak_diathesis_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T2)
w <- weights(weak_diathesis_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1111912
##
## $adj.R.squared
## [1] 0.09143991
strong_diathesis_T2 <- nls(wb_T2 ~ B0 + 0*(ev_T2 + 3) + B3*((ev_T2 + 3)*hsc_T2),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T2 + 3) + B3 * ((ev_T2 + 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.33356 0.21094 11.062 < 2e-16 ***
## B3 0.03224 0.01006 3.207 0.00185 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8622 on 91 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.172e-07
## (21 observations deleted due to missingness)
AIC(strong_diathesis_T2)
## [1] 240.3212
BIC(strong_diathesis_T2)
## [1] 247.919
pred <- predict(strong_diathesis_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T2)
w <- weights(strong_diathesis_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1015265
##
## $adj.R.squared
## [1] 0.09165313
weak_vs_T2 <- nls(wb_T2 ~ B0 + B1*(ev_T2 - 3) + B3*((ev_T2 - 3)*hsc_T2), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T2 - 3) + B3 * ((ev_T2 - 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.33441 0.14904 22.373 <2e-16 ***
## B1 -0.10358 0.20259 -0.511 0.610
## B3 0.05461 0.03737 1.461 0.147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8524 on 90 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.294e-07
## (21 observations deleted due to missingness)
AIC(weak_vs_T2)
## [1] 239.1776
BIC(weak_vs_T2)
## [1] 249.308
pred <- predict(weak_vs_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T2)
w <- weights(weak_vs_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.131389
##
## $adj.R.squared
## [1] 0.1120866
strong_vs_T2 <- nls(wb_T2 ~ B0 + 0*(ev_T2 - 3) + B3*((ev_T2 - 3)*hsc_T2),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T2 - 3) + B3 * ((ev_T2 - 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.356185 0.142247 23.594 < 2e-16 ***
## B3 0.036180 0.009861 3.669 0.00041 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.849 on 91 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 7.884e-08
## (21 observations deleted due to missingness)
AIC(strong_vs_T2)
## [1] 237.4473
BIC(strong_vs_T2)
## [1] 245.0451
pred <- predict(strong_vs_T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T2)
w <- weights(strong_vs_T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1288662
##
## $adj.R.squared
## [1] 0.1192933
weak_diff_T3 <- nls(wb_T3 ~ B0 + B1*(ev_T3 - C) + B3*((ev_T3 - C)*hsc_T3),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T3 - C) + B3 * ((ev_T3 - C) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.27024 0.41959 7.794 6.96e-12 ***
## B1 -0.23560 0.36636 -0.643 0.522
## C 2.76894 2.32002 1.193 0.236
## B3 0.07700 0.06606 1.166 0.247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8877 on 98 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 5.931e-07
## (12 observations deleted due to missingness)
AIC(weak_diff_T3)
## [1] 271.08
BIC(weak_diff_T3)
## [1] 284.2049
pred <- predict(weak_diff_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T3)
w <- weights(weak_diff_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1387073
##
## $adj.R.squared
## [1] 0.1123412
strong_diff_T3 <- nls(wb_T3 ~ B0 + 0*(ev_T3 - C) + B3*((ev_T3 - C)*hsc_T3),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T3 - C) + B3 * ((ev_T3 - C) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.71943 0.64349 5.780 8.69e-08 ***
## C 5.08683 3.86721 1.315 0.191422
## B3 0.03502 0.01012 3.462 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8851 on 99 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 4.928e-07
## (12 observations deleted due to missingness)
AIC(strong_diff_T3)
## [1] 269.5095
BIC(strong_diff_T3)
## [1] 280.0094
pred <- predict(strong_diff_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T3)
w <- weights(strong_diff_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1350728
##
## $adj.R.squared
## [1] 0.1175995
weak_diathesis_T3 <- nls(wb_T3 ~ B0 + B1*(ev_T3 + 3) + B3*((ev_T3 + 3)*hsc_T3), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T3 + 3) + B3 * ((ev_T3 + 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.16989 0.22446 9.667 5.82e-16 ***
## B1 0.29155 0.16057 1.816 0.0724 .
## B3 -0.01795 0.02908 -0.617 0.5385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8946 on 99 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 8.778e-08
## (12 observations deleted due to missingness)
AIC(weak_diathesis_T3)
## [1] 271.7034
BIC(weak_diathesis_T3)
## [1] 282.2033
pred <- predict(weak_diathesis_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T3)
w <- weights(weak_diathesis_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.116268
##
## $adj.R.squared
## [1] 0.09841479
strong_diathesis_T3 <- nls(wb_T3 ~ B0 + 0*(ev_T3 + 3) + B3*((ev_T3 + 3)*hsc_T3),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T3 + 3) + B3 * ((ev_T3 + 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.29177 0.21663 10.579 < 2e-16 ***
## B3 0.03155 0.01023 3.084 0.00264 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9048 on 100 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.82e-08
## (12 observations deleted due to missingness)
AIC(strong_diathesis_T3)
## [1] 273.0448
BIC(strong_diathesis_T3)
## [1] 280.9197
pred <- predict(strong_diathesis_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T3)
w <- weights(strong_diathesis_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.08683896
##
## $adj.R.squared
## [1] 0.07770735
weak_vs_T3 <- nls(wb_T3 ~ B0 + B1*(ev_T3 - 3) + B3*((ev_T3 - 3)*hsc_T3), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T3 - 3) + B3 * ((ev_T3 - 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.30962 0.15706 21.072 <2e-16 ***
## B1 -0.21011 0.24384 -0.862 0.3909
## B3 0.07224 0.04198 1.721 0.0884 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8832 on 99 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.3e-08
## (12 observations deleted due to missingness)
AIC(weak_vs_T3)
## [1] 269.0891
BIC(weak_vs_T3)
## [1] 279.589
pred <- predict(weak_vs_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T3)
w <- weights(weak_vs_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1386304
##
## $adj.R.squared
## [1] 0.1212289
strong_vs_T3 <- nls(wb_T3 ~ B0 + 0*(ev_T3 - 3) + B3*((ev_T3 - 3)*hsc_T3),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T3 - 3) + B3 * ((ev_T3 - 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.358310 0.146356 22.946 < 2e-16 ***
## B3 0.037006 0.009482 3.903 0.000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8821 on 100 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 5.567e-08
## (12 observations deleted due to missingness)
AIC(strong_vs_T3)
## [1] 267.8513
BIC(strong_vs_T3)
## [1] 275.7262
pred <- predict(strong_vs_T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T3)
w <- weights(strong_vs_T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1321703
##
## $adj.R.squared
## [1] 0.123492
weak_diff_T4 <- nls(wb_T4 ~ B0 + B1*(ev_T4 - C) + B3*((ev_T4 - C)*hsc_T4),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T4 - C) + B3 * ((ev_T4 - C) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 1.61131 2.21956 0.726 0.470
## B1 0.46381 0.42723 1.086 0.280
## C -5.95743 10.97203 -0.543 0.588
## B3 -0.05298 0.08312 -0.637 0.525
##
## Residual standard error: 0.961 on 95 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 5.251e-06
## (15 observations deleted due to missingness)
AIC(weak_diff_T4)
## [1] 278.9991
BIC(weak_diff_T4)
## [1] 291.9747
pred <- predict(weak_diff_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T4)
w <- weights(weak_diff_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1276512
##
## $adj.R.squared
## [1] 0.1001033
strong_diff_T4 <- nls(wb_T4 ~ B0 + 0*(ev_T4 - C) + B3*((ev_T4 - C)*hsc_T4),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T4 - C) + B3 * ((ev_T4 - C) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 4.74160 0.68412 6.931 4.81e-10 ***
## C 10.78444 4.83217 2.232 0.02795 *
## B3 0.03604 0.01364 2.641 0.00964 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9619 on 96 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 3.804e-08
## (15 observations deleted due to missingness)
AIC(strong_diff_T4)
## [1] 278.2198
BIC(strong_diff_T4)
## [1] 288.6003
pred <- predict(strong_diff_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T4)
w <- weights(strong_diff_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1168286
##
## $adj.R.squared
## [1] 0.09842921
weak_diathesis_T4 <- nls(wb_T4 ~ B0 + B1*(ev_T4 + 3) + B3*((ev_T4 + 3)*hsc_T4), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T4 + 3) + B3 * ((ev_T4 + 3) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.19774 0.28466 7.720 1.1e-11 ***
## B1 0.63883 0.18101 3.529 0.000642 ***
## B3 -0.08784 0.03117 -2.818 0.005860 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9571 on 96 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 5.765e-08
## (15 observations deleted due to missingness)
AIC(weak_diathesis_T4)
## [1] 277.2124
BIC(weak_diathesis_T4)
## [1] 287.5929
pred <- predict(weak_diathesis_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T4)
w <- weights(weak_diathesis_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1257695
##
## $adj.R.squared
## [1] 0.1075564
strong_diathesis_T4 <- nls(wb_T4 ~ B0 + 0*(ev_T4 + 3) + B3*((ev_T4 + 3)*hsc_T4),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T4 + 3) + B3 * ((ev_T4 + 3) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.61026 0.27446 9.511 1.53e-15 ***
## B3 0.01384 0.01257 1.101 0.274
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.012 on 97 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 7.469e-08
## (15 observations deleted due to missingness)
AIC(strong_diathesis_T4)
## [1] 287.2897
BIC(strong_diathesis_T4)
## [1] 295.0751
pred <- predict(strong_diathesis_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T4)
w <- weights(strong_diathesis_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.01234146
##
## $adj.R.squared
## [1] 0.002159416
weak_vs_T4 <- nls(wb_T4 ~ B0 + B1*(ev_T4 - 3) + B3*((ev_T4 - 3)*hsc_T4), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T4 - 3) + B3 * ((ev_T4 - 3) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.28953 0.18107 18.167 <2e-16 ***
## B1 -0.28044 0.25524 -1.099 0.2746
## B3 0.09128 0.05001 1.825 0.0711 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.979 on 96 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 5.431e-08
## (15 observations deleted due to missingness)
AIC(weak_vs_T4)
## [1] 281.7058
BIC(weak_vs_T4)
## [1] 292.0863
pred <- predict(weak_vs_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T4)
w <- weights(weak_vs_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.08517603
##
## $adj.R.squared
## [1] 0.0661172
strong_vs_T4 <- nls(wb_T4 ~ B0 + 0*(ev_T4 - 3) + B3*((ev_T4 - 3)*hsc_T4),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T4 - 3) + B3 * ((ev_T4 - 3) * hsc_T4)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.31028 0.18028 18.362 < 2e-16 ***
## B3 0.03848 0.01385 2.777 0.00658 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9801 on 97 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 3.238e-08
## (15 observations deleted due to missingness)
AIC(strong_vs_T4)
## [1] 280.943
BIC(strong_vs_T4)
## [1] 288.7284
pred <- predict(strong_vs_T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T4)
w <- weights(strong_vs_T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.0736715
##
## $adj.R.squared
## [1] 0.06412173
weak_diff_om <- nls(wb_onemonth ~ B0 + B1*(ev_onemonth - C) + B3*((ev_onemonth - C)*hsc_onemonth),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_om)
##
## Formula: wb_onemonth ~ B0 + B1 * (ev_onemonth - C) + B3 * ((ev_onemonth -
## C) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.1659 0.3013 10.507 <2e-16 ***
## B1 -0.5786 0.6553 -0.883 0.3801
## C 1.9091 1.0590 1.803 0.0755 .
## B3 0.1596 0.1235 1.292 0.2004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7693 on 75 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 8.697e-07
## (35 observations deleted due to missingness)
AIC(weak_diff_om)
## [1] 188.6542
BIC(weak_diff_om)
## [1] 200.5014
pred <- predict(weak_diff_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_om)
w <- weights(weak_diff_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1302958
##
## $adj.R.squared
## [1] 0.09550759
strong_diff_om <- nls(wb_onemonth ~ B0 + 0*(ev_onemonth - C) + B3*((ev_onemonth - C)*hsc_onemonth),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_om)
##
## Formula: wb_onemonth ~ B0 + 0 * (ev_onemonth - C) + B3 * ((ev_onemonth -
## C) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.68529 0.67339 5.473 5.47e-07 ***
## C 3.77317 2.62898 1.435 0.15533
## B3 0.05152 0.01675 3.076 0.00291 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7682 on 76 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 4.179e-07
## (35 observations deleted due to missingness)
AIC(strong_diff_om)
## [1] 187.4712
BIC(strong_diff_om)
## [1] 196.9489
pred <- predict(strong_diff_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_om)
w <- weights(strong_diff_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1212552
##
## $adj.R.squared
## [1] 0.09813036
weak_diathesis_om <- nls(wb_onemonth ~ B0 + B1*(ev_onemonth + 3) + B3*((ev_onemonth + 3)*hsc_onemonth), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_om)
##
## Formula: wb_onemonth ~ B0 + B1 * (ev_onemonth + 3) + B3 * ((ev_onemonth +
## 3) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 1.90191 0.35741 5.321 1.01e-06 ***
## B1 0.37906 0.18951 2.000 0.0491 *
## B3 -0.02281 0.03128 -0.729 0.4680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.776 on 76 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.535e-07
## (35 observations deleted due to missingness)
AIC(weak_diathesis_om)
## [1] 189.0681
BIC(weak_diathesis_om)
## [1] 198.5458
pred <- predict(weak_diathesis_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_om)
w <- weights(weak_diathesis_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1033116
##
## $adj.R.squared
## [1] 0.07971451
strong_diathesis_om <- nls(wb_onemonth ~ B0 + 0*(ev_onemonth + 3) + B3*((ev_onemonth + 3)*hsc_onemonth),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_om)
##
## Formula: wb_onemonth ~ B0 + 0 * (ev_onemonth + 3) + B3 * ((ev_onemonth +
## 3) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.26128 0.31493 7.180 3.78e-10 ***
## B3 0.03229 0.01509 2.139 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.791 on 77 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.294e-07
## (35 observations deleted due to missingness)
AIC(strong_diathesis_om)
## [1] 191.121
BIC(strong_diathesis_om)
## [1] 198.2293
pred <- predict(strong_diathesis_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_om)
w <- weights(strong_diathesis_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.05610817
##
## $adj.R.squared
## [1] 0.04384983
weak_vs_om <- nls(wb_onemonth ~ B0 + B1*(ev_onemonth - 3) + B3*((ev_onemonth - 3)*hsc_onemonth), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_om)
##
## Formula: wb_onemonth ~ B0 + B1 * (ev_onemonth - 3) + B3 * ((ev_onemonth -
## 3) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.45073 0.20854 16.547 <2e-16 ***
## B1 -0.21576 0.31261 -0.690 0.492
## B3 0.09069 0.05747 1.578 0.119
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7663 on 76 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 9.08e-08
## (35 observations deleted due to missingness)
AIC(weak_vs_om)
## [1] 187.0722
BIC(weak_vs_om)
## [1] 196.5499
pred <- predict(weak_vs_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_om)
w <- weights(weak_vs_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1256822
##
## $adj.R.squared
## [1] 0.1026738
strong_vs_om <- nls(wb_onemonth ~ B0 + 0*(ev_onemonth - 3) + B3*((ev_onemonth - 3)*hsc_onemonth),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_om)
##
## Formula: wb_onemonth ~ B0 + 0 * (ev_onemonth - 3) + B3 * ((ev_onemonth -
## 3) * hsc_onemonth)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.49127 0.19941 17.508 < 2e-16 ***
## B3 0.05265 0.01623 3.243 0.00175 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7637 on 77 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 9.248e-08
## (35 observations deleted due to missingness)
AIC(strong_vs_om)
## [1] 185.5658
BIC(strong_vs_om)
## [1] 192.6741
pred <- predict(strong_vs_om) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_om)
w <- weights(strong_vs_om)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_om)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.1202019
##
## $adj.R.squared
## [1] 0.1087759
HowToCreateWidamanFigure
# 1時点目は強い素因ストレスモデルが支持された
# B0(切片)=2.12
# B1(傾き:低感受性群)= 0.00
# C(交差点)=3.00
# B3(傾き:高感受性群)= 0.03
#png("figure/week1.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T1, wb_T1)) +
geom_abline(intercept = 2.03, slope = 0.03, size = 1) +
geom_abline(intercept = 2.12, slope = 0.00, size = 1, linetype = 2) +
ylim(1.5, 2.5) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# 2時点目は強いヴァンテージ感受性モデルが支持された
# B0(切片)= 3.36
# B1(傾き:低感受性群)= 0.00
# C(交差点)= -3.00
# B3(傾き:高感受性群)= 0.04
#png("figure/week2.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T2, wb_T2)) +
geom_abline(intercept = 3.48, slope = 0.04, size = 1) +
geom_abline(intercept = 3.36, slope = 0.00, size = 1, linetype = 2) +
ylim(3.0, 4.0) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# 3時点目は強いヴァンテージ感受性モデルが支持された
# B0(切片)= 3.36
# B1(傾き:低感受性群)= 0.00
# C(交差点)= -3.00
# B3(傾き:高感受性群)= 0.04
#png("figure/week3.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T3, wb_T3)) +
geom_abline(intercept = 3.48, slope = 0.04, size = 1) +
geom_abline(intercept = 3.36, slope = 0.00, size = 1, linetype = 2) +
ylim(3.0, 4.0) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# 4時点目は弱い素因ストレスモデルが支持された
# B0(切片)= 2.20
# B1(傾き:低感受性群)= 0.64
# C(交差点)= 3.00
# B3(傾き:高感受性群)= -0.09
#png("figure/week4.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T4, wb_T4)) +
geom_abline(intercept = 2.47, slope = -0.09, size = 1) +
geom_abline(intercept = 0.28, slope = 0.64, size = 1, linetype = 2) +
ylim(0.0, 3.0) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# 1か月全体では強いヴァンテージ感受性モデルが支持された
# B0(切片)= 3.49
# B1(傾き:低感受性群)= 0.00
# C(交差点)= 3.00
# B3(傾き:高感受性群)= 0.05
#png("figure/week3.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_onemonth, wb_onemonth)) +
geom_abline(intercept = 3.64, slope = 0.05, size = 1) +
geom_abline(intercept = 3.49, slope = 0.00, size = 1, linetype = 2) +
ylim(3.0, 4.0) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
WidamanPlot
#通常のlmで分析版(ステップ1:主効果モデル)
model_addt2s1 <- lm(data_c$wb_T2 ~ data_c$ev_T1_c + data_c$hsc_T1_c)
summary(model_addt2s1)
##
## Call:
## lm(formula = data_c$wb_T2 ~ data_c$ev_T1_c + data_c$hsc_T1_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.18953 -0.61511 0.01373 0.58483 2.09798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.962025 0.103823 28.530 <2e-16 ***
## data_c$ev_T1_c 0.116927 0.065621 1.782 0.0788 .
## data_c$hsc_T1_c 0.005885 0.144953 0.041 0.9677
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9228 on 76 degrees of freedom
## Multiple R-squared: 0.04044, Adjusted R-squared: 0.01519
## F-statistic: 1.601 on 2 and 76 DF, p-value: 0.2083
AIC(model_addt2s1)
## [1] 216.4398
BIC(model_addt2s1)
## [1] 225.9176
#通常のlmで分析版(ステップ2:交互作用モデル)
model_addt2s2 <- lm(data_c$wb_T2 ~ data_c$ev_T1_c + data_c$hsc_T1_c + data_c$ev_T1_c:data_c$hsc_T1_c)
summary(model_addt2s2)
##
## Call:
## lm(formula = data_c$wb_T2 ~ data_c$ev_T1_c + data_c$hsc_T1_c +
## data_c$ev_T1_c:data_c$hsc_T1_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.16674 -0.61954 0.03249 0.57250 2.09993
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.965563 0.105078 28.222 <2e-16 ***
## data_c$ev_T1_c 0.115262 0.066237 1.740 0.0859 .
## data_c$hsc_T1_c -0.006603 0.151369 -0.044 0.9653
## data_c$ev_T1_c:data_c$hsc_T1_c 0.026571 0.086373 0.308 0.7592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9283 on 75 degrees of freedom
## Multiple R-squared: 0.04165, Adjusted R-squared: 0.003314
## F-statistic: 1.086 on 3 and 75 DF, p-value: 0.3601
AIC(model_addt2s2)
## [1] 218.3402
BIC(model_addt2s2)
## [1] 230.1874
anova(model_addt2s1, model_addt2s2) #R^2の増加量の検定
#通常のlmで分析版(ステップ1:主効果モデル)
model_addt3s1 <- lm(data_c$wb_T3 ~ data_c$ev_T2_c + data_c$hsc_T2_c)
summary(model_addt3s1)
##
## Call:
## lm(formula = data_c$wb_T3 ~ data_c$ev_T2_c + data_c$hsc_T2_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.30406 -0.60488 -0.00416 0.56075 2.21841
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.93418 0.10109 29.027 <2e-16 ***
## data_c$ev_T2_c 0.14984 0.06265 2.392 0.0193 *
## data_c$hsc_T2_c -0.06217 0.13355 -0.466 0.6429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8985 on 76 degrees of freedom
## Multiple R-squared: 0.07349, Adjusted R-squared: 0.04911
## F-statistic: 3.014 on 2 and 76 DF, p-value: 0.05498
AIC(model_addt3s1)
## [1] 212.2184
BIC(model_addt3s1)
## [1] 221.6962
#通常のlmで分析版(ステップ2:交互作用モデル)
model_addt3s2 <- lm(data_c$wb_T3 ~ data_c$ev_T2_c + data_c$hsc_T2_c + data_c$ev_T2_c:data_c$hsc_T2_c)
summary(model_addt3s2)
##
## Call:
## lm(formula = data_c$wb_T3 ~ data_c$ev_T2_c + data_c$hsc_T2_c +
## data_c$ev_T2_c:data_c$hsc_T2_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.32889 -0.56732 -0.02328 0.57998 2.24230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.93970 0.10062 29.216 <2e-16 ***
## data_c$ev_T2_c 0.12658 0.06464 1.958 0.0539 .
## data_c$hsc_T2_c -0.04409 0.13349 -0.330 0.7421
## data_c$ev_T2_c:data_c$hsc_T2_c 0.11992 0.08855 1.354 0.1797
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8936 on 75 degrees of freedom
## Multiple R-squared: 0.09561, Adjusted R-squared: 0.05943
## F-statistic: 2.643 on 3 and 75 DF, p-value: 0.05538
AIC(model_addt3s2)
## [1] 212.3099
BIC(model_addt3s2)
## [1] 224.1572
anova(model_addt3s1, model_addt3s2) #R^2の増加量の検定
#通常のlmで分析版(ステップ1:主効果モデル)
model_addt4s1 <- lm(data_c$wb_T4 ~ data_c$ev_T3_c + data_c$hsc_T3_c)
summary(model_addt4s1)
##
## Call:
## lm(formula = data_c$wb_T4 ~ data_c$ev_T3_c + data_c$hsc_T3_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3248 -0.6979 -0.1591 0.6893 2.1530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.89114 0.11578 24.971 <2e-16 ***
## data_c$ev_T3_c 0.11250 0.07469 1.506 0.136
## data_c$hsc_T3_c -0.05158 0.15366 -0.336 0.738
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.029 on 76 degrees of freedom
## Multiple R-squared: 0.03342, Adjusted R-squared: 0.007984
## F-statistic: 1.314 on 2 and 76 DF, p-value: 0.2748
AIC(model_addt4s1)
## [1] 233.6596
BIC(model_addt4s1)
## [1] 243.1374
#通常のlmで分析版(ステップ2:交互作用モデル)
model_addt4s2 <- lm(data_c$wb_T4 ~ data_c$ev_T3_c + data_c$hsc_T3_c + data_c$ev_T3_c:data_c$hsc_T3_c)
summary(model_addt4s2)
##
## Call:
## lm(formula = data_c$wb_T4 ~ data_c$ev_T3_c + data_c$hsc_T3_c +
## data_c$ev_T3_c:data_c$hsc_T3_c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3585 -0.7028 -0.1558 0.6931 2.1448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.90046 0.11756 24.673 <2e-16 ***
## data_c$ev_T3_c 0.10405 0.07661 1.358 0.179
## data_c$hsc_T3_c -0.04964 0.15442 -0.321 0.749
## data_c$ev_T3_c:data_c$hsc_T3_c 0.04576 0.08368 0.547 0.586
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.034 on 75 degrees of freedom
## Multiple R-squared: 0.03726, Adjusted R-squared: -0.00125
## F-statistic: 0.9675 on 3 and 75 DF, p-value: 0.4126
AIC(model_addt4s2)
## [1] 235.3453
BIC(model_addt4s2)
## [1] 247.1925
anova(model_addt4s1, model_addt4s2) #R^2の増加量の検定
weak_diff_T1T2 <- nls(wb_T2 ~ B0 + B1*(ev_T1 - C) + B3*((ev_T1 - C)*hsc_T1),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T1T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T1 - C) + B3 * ((ev_T1 - C) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 -9.245e+00 1.611e+03 -0.006 0.995
## B1 1.361e-01 4.012e-01 0.339 0.735
## C -9.076e+01 1.211e+04 -0.007 0.994
## B3 -5.940e-04 7.810e-02 -0.008 0.994
##
## Residual standard error: 0.9085 on 93 degrees of freedom
##
## Number of iterations to convergence: 3
## Achieved convergence tolerance: 2.258e-06
## (17 observations deleted due to missingness)
AIC(weak_diff_T1T2)
## [1] 262.5684
BIC(weak_diff_T1T2)
## [1] 275.4419
pred <- predict(weak_diff_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T1T2)
w <- weights(weak_diff_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.05224564
##
## $adj.R.squared
## [1] 0.02167292
strong_diff_T1T2 <- nls(wb_T2 ~ B0 + 0*(ev_T1 - C) + B3*((ev_T1 - C)*hsc_T1),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T1T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T1 - C) + B3 * ((ev_T1 - C) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.28927 0.63085 5.214 1.09e-06 ***
## C 3.45608 5.04675 0.685 0.495
## B3 0.02559 0.01182 2.165 0.033 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9042 on 94 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 1.772e-06
## (17 observations deleted due to missingness)
AIC(strong_diff_T1T2)
## [1] 260.6883
BIC(strong_diff_T1T2)
## [1] 270.9872
pred <- predict(strong_diff_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T1T2)
w <- weights(strong_diff_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.05107284
##
## $adj.R.squared
## [1] 0.0308829
weak_diathesis_T1T2 <- nls(wb_T2 ~ B0 + B1*(ev_T1 - 3) + B3*((ev_T1 - 3)*hsc_T1),
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T1T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T1 - 3) + B3 * ((ev_T1 - 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.23942 0.15321 21.144 <2e-16 ***
## B1 0.04910 0.28084 0.175 0.862
## B3 0.01674 0.05333 0.314 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9041 on 94 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.146e-08
## (17 observations deleted due to missingness)
AIC(weak_diathesis_T1T2)
## [1] 260.6654
BIC(weak_diathesis_T1T2)
## [1] 270.9642
pred <- predict(weak_diathesis_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T1T2)
w <- weights(weak_diathesis_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.0512971
##
## $adj.R.squared
## [1] 0.03111194
strong_diathesis_T1T2 <- nls(wb_T2 ~ B0 + 0*(ev_T1 - 3) + B3*((ev_T1 - 3)*hsc_T1),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T1T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T1 - 3) + B3 * ((ev_T1 - 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.23329 0.14838 21.790 <2e-16 ***
## B3 0.02584 0.01144 2.259 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8995 on 95 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 5.69e-08
## (17 observations deleted due to missingness)
AIC(strong_diathesis_T1T2)
## [1] 258.6969
BIC(strong_diathesis_T1T2)
## [1] 266.4211
pred <- predict(strong_diathesis_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T1T2)
w <- weights(strong_diathesis_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.05098863
##
## $adj.R.squared
## [1] 0.04099904
weak_vs_T1T2 <- nls(wb_T2 ~ B0 + B1*(ev_T1 + 3) + B3*((ev_T1 + 3)*hsc_T1), #+3は環境変数の最小値(C on X)→ -(c)なので+3となる
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T1T2)
##
## Formula: wb_T2 ~ B0 + B1 * (ev_T1 + 3) + B3 * ((ev_T1 + 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.43476 0.25788 9.441 2.83e-15 ***
## B1 0.19040 0.14652 1.299 0.197
## B3 -0.01124 0.02715 -0.414 0.680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9037 on 94 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.7e-08
## (17 observations deleted due to missingness)
AIC(weak_vs_T1T2)
## [1] 260.5904
BIC(weak_vs_T1T2)
## [1] 270.8893
pred <- predict(weak_vs_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T1T2)
w <- weights(weak_vs_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.05203008
##
## $adj.R.squared
## [1] 0.0318605
strong_vs_T1T2 <- nls(wb_T2 ~ B0 + 0*(ev_T1 + 3) + B3*((ev_T1 + 3)*hsc_T1),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T1T2)
##
## Formula: wb_T2 ~ B0 + 0 * (ev_T1 + 3) + B3 * ((ev_T1 + 3) * hsc_T1)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.55376 0.24195 10.555 <2e-16 ***
## B3 0.02090 0.01126 1.856 0.0665 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.907 on 95 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.977e-08
## (17 observations deleted due to missingness)
AIC(strong_vs_T1T2)
## [1] 260.3175
BIC(strong_vs_T1T2)
## [1] 268.0416
pred <- predict(strong_vs_T1T2) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T1T2)
w <- weights(strong_vs_T1T2)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T1T2)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.03500062
##
## $adj.R.squared
## [1] 0.02484274
weak_diff_T2T3 <- nls(wb_T3 ~ B0 + B1*(ev_T2 - C) + B3*((ev_T2 - C)*hsc_T2),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T2T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T2 - C) + B3 * ((ev_T2 - C) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.97074 0.17526 16.950 <2e-16 ***
## B1 -0.45735 0.44327 -1.032 0.305
## C 1.18350 1.18338 1.000 0.320
## B3 0.11343 0.08266 1.372 0.174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8706 on 83 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 4.556e-06
## (27 observations deleted due to missingness)
AIC(weak_diff_T2T3)
## [1] 228.6799
BIC(weak_diff_T2T3)
## [1] 241.0094
pred <- predict(weak_diff_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T2T3)
w <- weights(weak_diff_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09919759
##
## $adj.R.squared
## [1] 0.06663847
strong_diff_T2T3 <- nls(wb_T3 ~ B0 + 0*(ev_T2 - C) + B3*((ev_T2 - C)*hsc_T2),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T2T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T2 - C) + B3 * ((ev_T2 - C) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.19621 0.65888 4.851 5.59e-06 ***
## C 2.68963 4.52401 0.595 0.55376
## B3 0.02883 0.01046 2.756 0.00717 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8709 on 84 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 6.624e-07
## (27 observations deleted due to missingness)
AIC(strong_diff_T2T3)
## [1] 227.7886
BIC(strong_diff_T2T3)
## [1] 237.6522
pred <- predict(strong_diff_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T2T3)
w <- weights(strong_diff_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.08764425
##
## $adj.R.squared
## [1] 0.0659215
weak_diathesis_T2T3 <- nls(wb_T3 ~ B0 + B1*(ev_T2 - 3) + B3*((ev_T2 - 3)*hsc_T2), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T2T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T2 - 3) + B3 * ((ev_T2 - 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.21285 0.16027 20.046 <2e-16 ***
## B1 -0.13246 0.25062 -0.529 0.599
## B3 0.05193 0.04521 1.149 0.254
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8695 on 84 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.081e-07
## (27 observations deleted due to missingness)
AIC(weak_diathesis_T2T3)
## [1] 227.5046
BIC(weak_diathesis_T2T3)
## [1] 237.3682
pred <- predict(weak_diathesis_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T2T3)
w <- weights(weak_diathesis_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09061783
##
## $adj.R.squared
## [1] 0.06896587
strong_diathesis_T2T3 <- nls(wb_T3 ~ B0 + 0*(ev_T2 - 3) + B3*((ev_T2 - 3)*hsc_T2),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T2T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T2 - 3) + B3 * ((ev_T2 - 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.23991 0.15123 21.424 < 2e-16 ***
## B3 0.02864 0.01002 2.857 0.00538 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8658 on 85 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 8.105e-08
## (27 observations deleted due to missingness)
AIC(strong_diathesis_T2T3)
## [1] 225.7934
BIC(strong_diathesis_T2T3)
## [1] 233.1911
pred <- predict(strong_diathesis_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T2T3)
w <- weights(strong_diathesis_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.08759377
##
## $adj.R.squared
## [1] 0.07685958
weak_vs_T2T3 <- nls(wb_T3 ~ B0 + B1*(ev_T2 + 3) + B3*((ev_T2 + 3)*hsc_T2), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T2T3)
##
## Formula: wb_T3 ~ B0 + B1 * (ev_T2 + 3) + B3 * ((ev_T2 + 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.351255 0.228560 10.287 <2e-16 ***
## B1 0.132347 0.176366 0.750 0.455
## B3 0.003054 0.032224 0.095 0.925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8762 on 84 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.505e-07
## (27 observations deleted due to missingness)
AIC(weak_vs_T2T3)
## [1] 228.8512
BIC(weak_vs_T2T3)
## [1] 238.7148
pred <- predict(weak_vs_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T2T3)
w <- weights(weak_vs_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.07643295
##
## $adj.R.squared
## [1] 0.05444326
strong_vs_T2T3 <- nls(wb_T3 ~ B0 + 0*(ev_T2 + 3) + B3*((ev_T2 + 3)*hsc_T2),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T2T3)
##
## Formula: wb_T3 ~ B0 + 0 * (ev_T2 + 3) + B3 * ((ev_T2 + 3) * hsc_T2)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.40765 0.21530 11.183 <2e-16 ***
## B3 0.02597 0.01025 2.534 0.0131 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.874 on 85 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 1.361e-07
## (27 observations deleted due to missingness)
AIC(strong_vs_T2T3)
## [1] 227.4324
BIC(strong_vs_T2T3)
## [1] 234.8302
pred <- predict(strong_vs_T2T3) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T2T3)
w <- weights(strong_vs_T2T3)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T2T3)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.07024157
##
## $adj.R.squared
## [1] 0.05930324
weak_diff_T3T4 <- nls(wb_T4 ~ B0 + B1*(ev_T3 - C) + B3*((ev_T3 - C)*hsc_T3),
data = data,
start = list(B0 = 90, B1 = 0, C = 20, B3 = -1))
summary(weak_diff_T3T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T3 - C) + B3 * ((ev_T3 - C) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.19170 0.81504 3.916 0.000172 ***
## B1 -0.04837 0.43683 -0.111 0.912067
## C 2.36499 4.38604 0.539 0.591032
## B3 0.04440 0.07909 0.561 0.575888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.048 on 93 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 2.084e-06
## (17 observations deleted due to missingness)
AIC(weak_diff_T3T4)
## [1] 290.2987
BIC(weak_diff_T3T4)
## [1] 303.1722
pred <- predict(weak_diff_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diff_T3T4)
w <- weights(weak_diff_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diff_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09417401
##
## $adj.R.squared
## [1] 0.06495382
strong_diff_T3T4 <- nls(wb_T4 ~ B0 + 0*(ev_T3 - C) + B3*((ev_T3 - C)*hsc_T3),
data = data,
start = list(B0 = 90, C = 20, B3 = -1))
summary(strong_diff_T3T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T3 - C) + B3 * ((ev_T3 - C) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.26609 0.76899 4.247 5.09e-05 ***
## C 2.74578 4.24744 0.646 0.51956
## B3 0.03575 0.01223 2.923 0.00434 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 94 degrees of freedom
##
## Number of iterations to convergence: 2
## Achieved convergence tolerance: 4.965e-07
## (17 observations deleted due to missingness)
AIC(strong_diff_T3T4)
## [1] 288.3115
BIC(strong_diff_T3T4)
## [1] 298.6103
pred <- predict(strong_diff_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diff_T3T4)
w <- weights(strong_diff_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diff_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09405458
##
## $adj.R.squared
## [1] 0.07477915
weak_diathesis_T3T4 <- nls(wb_T4 ~ B0 + B1*(ev_T3 - 3) + B3*((ev_T3 - 3)*hsc_T3), #+3は環境変数の最大値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1)) #Cの初期値は設定不要
summary(weak_diathesis_T3T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T3 - 3) + B3 * ((ev_T3 - 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.308476 0.190535 17.364 <2e-16 ***
## B1 -0.008593 0.289132 -0.030 0.976
## B3 0.036915 0.049646 0.744 0.459
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.043 on 94 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 8.2e-08
## (17 observations deleted due to missingness)
AIC(weak_diathesis_T3T4)
## [1] 288.3142
BIC(weak_diathesis_T3T4)
## [1] 298.613
pred <- predict(weak_diathesis_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_diathesis_T3T4)
w <- weights(weak_diathesis_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_diathesis_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09402908
##
## $adj.R.squared
## [1] 0.0747531
strong_diathesis_T3T4 <- nls(wb_T4 ~ B0 + 0*(ev_T3 - 3) + B3*((ev_T3 - 3)*hsc_T3),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_diathesis_T3T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T3 - 3) + B3 * ((ev_T3 - 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 3.31054 0.17645 18.76 < 2e-16 ***
## B3 0.03548 0.01130 3.14 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.037 on 95 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 5.754e-08
## (17 observations deleted due to missingness)
AIC(strong_diathesis_T3T4)
## [1] 286.3151
BIC(strong_diathesis_T3T4)
## [1] 294.0392
pred <- predict(strong_diathesis_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_diathesis_T3T4)
w <- weights(strong_diathesis_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_diathesis_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.09402056
##
## $adj.R.squared
## [1] 0.08448394
weak_vs_T3T4 <- nls(wb_T4 ~ B0 + B1*(ev_T3 + 3) + B3*((ev_T3 + 3)*hsc_T3), #-3は環境変数の最小値(C on X)
data = data,
start = list(B0 = 90, B1 = 0, B3 = -1))
summary(weak_vs_T3T4)
##
## Formula: wb_T4 ~ B0 + B1 * (ev_T3 + 3) + B3 * ((ev_T3 + 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.132789 0.265543 8.032 2.74e-12 ***
## B1 0.235887 0.190272 1.240 0.218
## B3 -0.006894 0.034935 -0.197 0.844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.045 on 94 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.827e-08
## (17 observations deleted due to missingness)
AIC(weak_vs_T3T4)
## [1] 288.8429
BIC(weak_vs_T3T4)
## [1] 299.1417
pred <- predict(weak_vs_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(weak_vs_T3T4)
w <- weights(weak_vs_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(weak_vs_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.0890777
##
## $adj.R.squared
## [1] 0.06969638
strong_vs_T3T4 <- nls(wb_T4 ~ B0 + 0*(ev_T3 + 3) + B3*((ev_T3 + 3)*hsc_T3),
data = data,
start = list(B0 = 90, B3 = -1))
summary(strong_vs_T3T4)
##
## Formula: wb_T4 ~ B0 + 0 * (ev_T3 + 3) + B3 * ((ev_T3 + 3) * hsc_T3)
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## B0 2.22409 0.25585 8.693 1.02e-13 ***
## B3 0.03370 0.01221 2.759 0.00696 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.048 on 95 degrees of freedom
##
## Number of iterations to convergence: 1
## Achieved convergence tolerance: 6.119e-08
## (17 observations deleted due to missingness)
AIC(strong_vs_T3T4)
## [1] 288.416
BIC(strong_vs_T3T4)
## [1] 296.1402
pred <- predict(strong_vs_T3T4) #非線形モデルで準R2を算出する
n <- length(pred)
res <- resid(strong_vs_T3T4)
w <- weights(strong_vs_T3T4)
if (is.null(w)) w <- rep(1, n)
rss <- sum(w * res ^ 2)
resp <- pred + res
center <- weighted.mean(resp, w)
r.df <- summary(strong_vs_T3T4)$df[2]
int.df <- 1
tss <- sum(w * (resp - center)^2)
r.sq <- 1 - rss/tss
adj.r.sq <- 1 - (1 - r.sq) * (n - int.df) / r.df
out <- list(pseudo.R.squared = r.sq,
adj.R.squared = adj.r.sq)
out
## $pseudo.R.squared
## [1] 0.07418374
##
## $adj.R.squared
## [1] 0.06443831
# T1->T2は強いヴァンテージ感受性モデルが支持された
# B0(切片)=3.23
# B1(傾き:低感受性群)= 0.00
# C(交差点)=3.00
# B3(傾き:高感受性群)= 0.03
#png("figure/ad1.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T1, wb_T2)) +
geom_abline(intercept = 3.32, slope = 0.03, size = 1) +
geom_abline(intercept = 3.23, slope = 0.00, size = 1, linetype = 2) +
ylim(3.00, 4.00) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# B0(切片)=3.24
# B1(傾き:低感受性群)= 0.00
# C(交差点)=3.00
# B3(傾き:高感受性群)= 0.03
#png("figure/ad2.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T2, wb_T3)) +
geom_abline(intercept = 3.33, slope = 0.03, size = 1) +
geom_abline(intercept = 3.24, slope = 0.00, size = 1, linetype = 2) +
ylim(3.00, 4.00) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
# B0(切片)=3.31
# B1(傾き:低感受性群)= 0.00
# C(交差点)=3.00
# B3(傾き:高感受性群)= 0.03
#png("figure/ad3.png", width = 1200, height = 1200)
p <- ggplot(data, aes(ev_T3, wb_T4)) +
geom_abline(intercept = 3.43, slope = 0.04, size = 1) +
geom_abline(intercept = 3.31, slope = 0.00, size = 1, linetype = 2) +
ylim(3.00, 4.00) + xlim(-3, 3)
p + theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
axis.line = element_line(colour = "azure4",
linetype = "solid"), axis.ticks = element_line(size = 1,linetype = "blank"))
#dev.off()
library(LEGIT)
## Loading required package: formula.tools
# https://cran.r-project.org/web/packages/LEGIT/vignettes/GxE_testing.html
df <- as.data.frame(data) #データフレームとして明示
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T1", drop=FALSE], env=df[,"ev_T1", drop = FALSE], formula_noGxE = wb_T1 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.12140 0.03211
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.74 AIC: 269.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 119.6
## Residual Deviance: 69.74 AIC: 267.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 80.3
## Residual Deviance: 69.71 AIC: 267.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 269.4123
##
## $true_model_parameters$AICc
## [1] 269.6366
##
## $true_model_parameters$BIC
## [1] 277.5409
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 109
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x00000000195407f8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.09068 0.03159
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.85 AIC: 269.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 92.2
## Residual Deviance: 69.85 AIC: 267.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 80.17
## Residual Deviance: 69.81 AIC: 267.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 269.5838
##
## $true_model_parameters$AICc
## [1] 269.8081
##
## $true_model_parameters$BIC
## [1] 277.7124
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 109
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x000000001a623758>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T1
## 2.5927 0.1765
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 78.04
## Residual Deviance: 69.24 AIC: 270
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.06101 0.12509 0.01038
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.11 AIC: 270.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 74.32
## Residual Deviance: 69.11 AIC: 266.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 80.25
## Residual Deviance: 69.11 AIC: 266.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 270.4063
##
## $true_model_parameters$AICc
## [1] 270.7837
##
## $true_model_parameters$BIC
## [1] 281.2445
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x000000001bf2d590>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T1 ev_T1
## 2.35719 0.04629 0.17892
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## (3 observations deleted due to missingness)
## Null Deviance: 78.03
## Residual Deviance: 69.11 AIC: 270.4
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.12607 0.23339 -0.01082
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.19 AIC: 270.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 71.81
## Residual Deviance: 69.19 AIC: 266.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 80.37
## Residual Deviance: 69.19 AIC: 266.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 270.5387
##
## $true_model_parameters$AICc
## [1] 270.9161
##
## $true_model_parameters$BIC
## [1] 281.3769
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x0000000017f02fa0>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.57411 0.03401
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.23 AIC: 268.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 80.84
## Residual Deviance: 69.23 AIC: 266.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## -0.1598 1.0000
##
## Degrees of Freedom: 111 Total (i.e. Null); 109 Residual
## Null Deviance: 80.84
## Residual Deviance: 69.23 AIC: 268.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 270.6064
##
## $true_model_parameters$AICc
## [1] 270.9837
##
## $true_model_parameters$BIC
## [1] 281.4445
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x000000001ba868b8>
##
## $crossover
## [1] -0.1597996
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 1.452928 0.147320 0.006115
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 69.1 AIC: 270.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 74.92
## Residual Deviance: 69.1 AIC: 266.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## -6.395 1.000
##
## Degrees of Freedom: 111 Total (i.e. Null); 109 Residual
## Null Deviance: 260
## Residual Deviance: 69.1 AIC: 268.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 272.4009
##
## $true_model_parameters$AICc
## [1] 272.9723
##
## $true_model_parameters$BIC
## [1] 285.9485
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 107
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x000000001ae58590>
##
## $crossover
## [1] -6.394522
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.735
##
## Degrees of Freedom: 112 Total (i.e. Null); 112 Residual
## (1 observation deleted due to missingness)
## Null Deviance: 78.12
## Residual Deviance: 78.12 AIC: 283
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T1
## 2.772872 -0.007356
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 78.1
## Residual Deviance: 78.09 AIC: 283.5
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "277.54" "-3" ""
## Diathesis-stress STRONG "277.71" "3" ""
## E only "278.13" NA ""
## Vantage sensitivity WEAK "281.24" "-3" ""
## G + E only "281.25" NA ""
## Diathesis-stress WEAK "281.38" "3" ""
## Differential susceptibility STRONG "281.44" "-0.16" "( -1.1 / 0.78 )"
## Differential susceptibility WEAK "285.95" "-6.39" "( -7.32 / -5.47 )"
## Intercept only "288.42" NA ""
## G only "291.61" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## Diathesis-stress STRONG ""
## E only ""
## Vantage sensitivity WEAK ""
## G + E only ""
## Diathesis-stress WEAK ""
## Differential susceptibility STRONG "Yes"
## Differential susceptibility WEAK "No"
## Intercept only ""
## G only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0.027027027027027"
## Diathesis-stress STRONG "1"
## E only NA
## Vantage sensitivity WEAK "0"
## G + E only NA
## Diathesis-stress WEAK "1"
## Differential susceptibility STRONG "0.225225225225225"
## Differential susceptibility WEAK "0"
## Intercept only NA
## G only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.77348 -0.57125 -0.01914 0.58977 1.86525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.121405 0.186187 11.394 < 2e-16 ***
## G:E 0.032112 0.008921 3.599 0.000481 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6397929)
##
## Null deviance: 78.027 on 110 degrees of freedom
## Residual deviance: 69.737 on 109 degrees of freedom
## AIC: 269.41
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.77348 -0.57125 -0.01914 0.58977 1.86525
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T1 1.0000 0.1133 8.827 2e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6397929)
##
## Null deviance: 78.027 on 111 degrees of freedom
## Residual deviance: 69.737 on 109 degrees of freedom
## AIC: 269.41
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.77934 -0.59527 -0.01914 0.59115 1.84911
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T1 1.0000 0.2458 4.069 8.96e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6395499)
##
## Null deviance: 78.027 on 111 degrees of freedom
## Residual deviance: 69.711 on 109 degrees of freedom
## AIC: 269.41
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits$vantage_sensitivity_STRONG, xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T2", drop=FALSE], env=df[,"ev_T2", drop = FALSE], formula_noGxE = wb_T2 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.35618 0.03618
##
## Degrees of Freedom: 92 Total (i.e. Null); 91 Residual
## Null Deviance: 75.29
## Residual Deviance: 65.59 AIC: 237.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 90.92
## Residual Deviance: 65.59 AIC: 235.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 76.65
## Residual Deviance: 65.59 AIC: 235.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 237.4473
##
## $true_model_parameters$AICc
## [1] 237.717
##
## $true_model_parameters$BIC
## [1] 245.0451
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 91
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001a9fb080>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.33356 0.03224
##
## Degrees of Freedom: 92 Total (i.e. Null); 91 Residual
## Null Deviance: 75.29
## Residual Deviance: 67.65 AIC: 240.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 110.2
## Residual Deviance: 67.65 AIC: 238.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 78.26
## Residual Deviance: 67.52 AIC: 238.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 240.3212
##
## $true_model_parameters$AICc
## [1] 240.5908
##
## $true_model_parameters$BIC
## [1] 247.919
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 91
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x0000000018d0df68>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.33441 -0.10358 0.05461
##
## Degrees of Freedom: 92 Total (i.e. Null); 90 Residual
## Null Deviance: 75.29
## Residual Deviance: 65.4 AIC: 239.2
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 123.1
## Residual Deviance: 65.4 AIC: 235.2
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 76.72
## Residual Deviance: 65.36 AIC: 235.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 239.1776
##
## $true_model_parameters$AICc
## [1] 239.6321
##
## $true_model_parameters$BIC
## [1] 249.308
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 90
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x0000000015479148>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.23686 0.03679
##
## Degrees of Freedom: 92 Total (i.e. Null); 91 Residual
## Null Deviance: 75.29
## Residual Deviance: 65.55 AIC: 237.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 83.15
## Residual Deviance: 65.55 AIC: 235.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## 2.321 1.000
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## Null Deviance: 83.15
## Residual Deviance: 65.55 AIC: 237.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 239.3994
##
## $true_model_parameters$AICc
## [1] 239.854
##
## $true_model_parameters$BIC
## [1] 249.5298
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 90
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001bf3b5c0>
##
## $crossover
## [1] 2.321332
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T2 ev_T2
## 3.1411 -0.0661 0.1799
##
## Degrees of Freedom: 92 Total (i.e. Null); 90 Residual
## (21 observations deleted due to missingness)
## Null Deviance: 75.29
## Residual Deviance: 66.7 AIC: 241
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.260342 0.152608 0.005785
##
## Degrees of Freedom: 92 Total (i.e. Null); 90 Residual
## Null Deviance: 75.29
## Residual Deviance: 66.92 AIC: 241.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 68.29
## Residual Deviance: 66.92 AIC: 237.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 77.32
## Residual Deviance: 66.92 AIC: 237.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 241.3154
##
## $true_model_parameters$AICc
## [1] 241.7699
##
## $true_model_parameters$BIC
## [1] 251.4458
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 90
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x000000001c146bd8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.0195 -0.3886 0.1103
##
## Degrees of Freedom: 92 Total (i.e. Null); 90 Residual
## Null Deviance: 75.29
## Residual Deviance: 64.6 AIC: 238
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 153.6
## Residual Deviance: 64.6 AIC: 234
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## 1.157 1.000
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## Null Deviance: 75.79
## Residual Deviance: 64.6 AIC: 236
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 240.0366
##
## $true_model_parameters$AICc
## [1] 240.7262
##
## $true_model_parameters$BIC
## [1] 252.6996
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 89
##
## $true_model_parameters$null.deviance
## [1] 75.29118
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x000000001b3f0ed0>
##
## $crossover
## [1] 1.157141
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T2
## 2.8339 0.1752
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## (20 observations deleted due to missingness)
## Null Deviance: 79.46
## Residual Deviance: 71.7 AIC: 247.3
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T2
## 3.4645 -0.1007
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## (17 observations deleted due to missingness)
## Null Deviance: 76.58
## Residual Deviance: 75.96 AIC: 257.6
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.972
##
## Degrees of Freedom: 98 Total (i.e. Null); 98 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 81.04 AIC: 265.1
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "245.05" "3" ""
## Vantage sensitivity STRONG "247.92" "-3" ""
## Diathesis-stress WEAK "249.31" "3" ""
## Differential susceptibility STRONG "249.53" "2.32" "( 1.32 / 3.32 )"
## G + E only "251.13" NA ""
## Vantage sensitivity WEAK "251.45" "-3" ""
## Differential susceptibility WEAK "252.7" "1.16" "( 0.2 / 2.12 )"
## E only "254.94" NA ""
## G only "265.29" NA ""
## Intercept only "270.32" NA ""
## Within observable range?
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## Diathesis-stress WEAK ""
## Differential susceptibility STRONG "No"
## G + E only ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "Yes"
## E only ""
## G only ""
## Intercept only ""
## % of observations below crossover
## Diathesis-stress STRONG "0.849462365591398"
## Vantage sensitivity STRONG "0"
## Diathesis-stress WEAK "0.849462365591398"
## Differential susceptibility STRONG "0.720430107526882"
## G + E only NA
## Vantage sensitivity WEAK "0.021505376344086"
## Differential susceptibility WEAK "0.623655913978495"
## E only NA
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.67448 -0.60785 -0.00282 0.54120 2.22270
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.356185 0.142247 23.594 < 2e-16 ***
## G:E 0.036180 0.009861 3.669 0.00041 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7207549)
##
## Null deviance: 75.291 on 92 degrees of freedom
## Residual deviance: 65.589 on 91 degrees of freedom
## AIC: 237.45
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.67448 -0.60785 -0.00282 0.54120 2.22270
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T2 1.0000 0.1687 5.928 5.42e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7207549)
##
## Null deviance: 75.291 on 93 degrees of freedom
## Residual deviance: 65.589 on 91 degrees of freedom
## AIC: 237.45
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.68005 -0.61189 -0.00523 0.53456 2.22270
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T2 1.0000 0.2553 3.917 0.000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7207329)
##
## Null deviance: 75.291 on 93 degrees of freedom
## Residual deviance: 65.587 on 91 degrees of freedom
## AIC: 237.45
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T3", drop=FALSE], env=df[,"ev_T3", drop = FALSE], formula_noGxE = wb_T3 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.35831 0.03701
##
## Degrees of Freedom: 101 Total (i.e. Null); 100 Residual
## Null Deviance: 89.66
## Residual Deviance: 77.81 AIC: 267.9
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 111.1
## Residual Deviance: 77.81 AIC: 265.9
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 89.28
## Residual Deviance: 77.8 AIC: 265.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 267.8513
##
## $true_model_parameters$AICc
## [1] 268.0962
##
## $true_model_parameters$BIC
## [1] 275.7262
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 100
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001546d450>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.30962 -0.21011 0.07224
##
## Degrees of Freedom: 101 Total (i.e. Null); 99 Residual
## Null Deviance: 89.66
## Residual Deviance: 77.23 AIC: 269.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 204.1
## Residual Deviance: 77.23 AIC: 265.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 88.04
## Residual Deviance: 77.23 AIC: 265.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 269.0891
##
## $true_model_parameters$AICc
## [1] 269.5015
##
## $true_model_parameters$BIC
## [1] 279.589
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 99
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x0000000018d5c890>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T3
## 2.7559 0.2036
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## (11 observations deleted due to missingness)
## Null Deviance: 90.86
## Residual Deviance: 79.67 AIC: 271.8
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.71943 0.03502
##
## Degrees of Freedom: 101 Total (i.e. Null); 100 Residual
## Null Deviance: 89.66
## Residual Deviance: 77.55 AIC: 267.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 158.1
## Residual Deviance: 77.55 AIC: 265.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## 5.087 1.000
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## Null Deviance: 158.1
## Residual Deviance: 77.55 AIC: 267.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 269.5095
##
## $true_model_parameters$AICc
## [1] 269.9219
##
## $true_model_parameters$BIC
## [1] 280.0094
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 99
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001b72db10>
##
## $crossover
## [1] 5.086837
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.29177 0.03155
##
## Degrees of Freedom: 101 Total (i.e. Null); 100 Residual
## Null Deviance: 89.66
## Residual Deviance: 81.87 AIC: 273
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 127.4
## Residual Deviance: 81.87 AIC: 271
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 93.68
## Residual Deviance: 81.64 AIC: 270.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 273.0448
##
## $true_model_parameters$AICc
## [1] 273.2897
##
## $true_model_parameters$BIC
## [1] 280.9197
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 100
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001c0c3310>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T3 ev_T3
## 3.5771 -0.1527 0.1864
##
## Degrees of Freedom: 101 Total (i.e. Null); 99 Residual
## (12 observations deleted due to missingness)
## Null Deviance: 89.66
## Residual Deviance: 78.29 AIC: 270.5
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.16989 0.29155 -0.01795
##
## Degrees of Freedom: 101 Total (i.e. Null); 99 Residual
## Null Deviance: 89.66
## Residual Deviance: 79.24 AIC: 271.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 93.97
## Residual Deviance: 79.24 AIC: 267.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 90.49
## Residual Deviance: 79.23 AIC: 267.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 271.7034
##
## $true_model_parameters$AICc
## [1] 272.1158
##
## $true_model_parameters$BIC
## [1] 282.2033
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 99
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001b891ff8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.2702 -0.2356 0.0770
##
## Degrees of Freedom: 101 Total (i.e. Null); 99 Residual
## Null Deviance: 89.66
## Residual Deviance: 77.22 AIC: 269.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 203.3
## Residual Deviance: 77.22 AIC: 265.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## 2.769 1.000
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## Null Deviance: 103.6
## Residual Deviance: 77.22 AIC: 267.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 271.08
##
## $true_model_parameters$AICc
## [1] 271.705
##
## $true_model_parameters$BIC
## [1] 284.2049
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 98
##
## $true_model_parameters$null.deviance
## [1] 89.66
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001aa0a2b8>
##
## $crossover
## [1] 2.76895
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T3
## 4.0307 -0.2128
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## (10 observations deleted due to missingness)
## Null Deviance: 90.47
## Residual Deviance: 87.9 AIC: 283.6
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.899
##
## Degrees of Freedom: 104 Total (i.e. Null); 104 Residual
## (9 observations deleted due to missingness)
## Null Deviance: 91.69
## Residual Deviance: 91.69 AIC: 287.7
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "275.73" "3" ""
## Diathesis-stress WEAK "279.59" "3" ""
## E only "279.75" NA ""
## Differential susceptibility STRONG "280.01" "5.09" "( 4.11 / 6.06 )"
## Vantage sensitivity STRONG "280.92" "-3" ""
## G + E only "280.98" NA ""
## Vantage sensitivity WEAK "282.2" "-3" ""
## Differential susceptibility WEAK "284.2" "2.77" "( 1.78 / 3.76 )"
## G only "291.58" NA ""
## Intercept only "293.05" NA ""
## Within observable range?
## Diathesis-stress STRONG ""
## Diathesis-stress WEAK ""
## E only ""
## Differential susceptibility STRONG "No"
## Vantage sensitivity STRONG ""
## G + E only ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## G only ""
## Intercept only ""
## % of observations below crossover
## Diathesis-stress STRONG "1"
## Diathesis-stress WEAK "0.852941176470588"
## E only NA
## Differential susceptibility STRONG "1"
## Vantage sensitivity STRONG "0.0588235294117647"
## G + E only NA
## Vantage sensitivity WEAK "0.0588235294117647"
## Differential susceptibility WEAK "0.852941176470588"
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.55831 -0.56319 -0.02603 0.50907 2.10426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.358310 0.146356 22.946 < 2e-16 ***
## G:E 0.037006 0.009482 3.903 0.000173 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7780962)
##
## Null deviance: 89.66 on 101 degrees of freedom
## Residual deviance: 77.81 on 100 degrees of freedom
## AIC: 267.85
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.55831 -0.56319 -0.02603 0.50907 2.10426
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T3 1.0000 0.1529 6.539 2.64e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7780962)
##
## Null deviance: 89.66 on 102 degrees of freedom
## Residual deviance: 77.81 on 100 degrees of freedom
## AIC: 267.85
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5380 -0.5581 -0.0195 0.5102 2.1072
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T3 1.0000 0.2603 3.842 0.000215 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7779687)
##
## Null deviance: 89.660 on 102 degrees of freedom
## Residual deviance: 77.797 on 100 degrees of freedom
## AIC: 267.85
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T4", drop=FALSE], env=df[,"ev_T4", drop = FALSE], formula_noGxE = wb_T4 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.19774 0.63883 -0.08784
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 87.93 AIC: 277.2
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 452.1
## Residual Deviance: 87.93 AIC: 273.2
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 97.51
## Residual Deviance: 87.92 AIC: 273.2
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 277.2124
##
## $true_model_parameters$AICc
## [1] 277.638
##
## $true_model_parameters$BIC
## [1] 287.5929
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001ad337a8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T4 ev_T4
## 4.5968 -0.3611 0.1952
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 100.6
## Residual Deviance: 88.12 AIC: 277.4
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 4.74160 0.03604
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 88.83 AIC: 276.2
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 439.7
## Residual Deviance: 88.83 AIC: 274.2
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T4
## 10.78 1.00
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 439.7
## Residual Deviance: 88.83 AIC: 276.2
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 278.2198
##
## $true_model_parameters$AICc
## [1] 278.6453
##
## $true_model_parameters$BIC
## [1] 288.6003
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001ac98eb0>
##
## $crossover
## [1] 10.78444
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.31028 0.03848
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 93.17 AIC: 280.9
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 118
## Residual Deviance: 93.17 AIC: 278.9
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 100.6
## Residual Deviance: 92.88 AIC: 278.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 280.943
##
## $true_model_parameters$AICc
## [1] 281.1957
##
## $true_model_parameters$BIC
## [1] 288.7284
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 97
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x0000000018711ca0>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 1.61129 0.46381 -0.05298
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 87.74 AIC: 277
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 465.3
## Residual Deviance: 87.74 AIC: 273
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T4
## -5.958 1.000
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 262.7
## Residual Deviance: 87.74 AIC: 275
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 278.9991
##
## $true_model_parameters$AICc
## [1] 279.6443
##
## $true_model_parameters$BIC
## [1] 291.9747
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x0000000013cdc7f8>
##
## $crossover
## [1] -5.95753
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.28953 -0.28044 0.09128
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 92.01 AIC: 281.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 231.7
## Residual Deviance: 92.01 AIC: 277.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 98.68
## Residual Deviance: 91.49 AIC: 277.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 281.7058
##
## $true_model_parameters$AICc
## [1] 282.1313
##
## $true_model_parameters$BIC
## [1] 292.0863
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001c40e638>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.61026 0.01384
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 99.34 AIC: 287.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 108.4
## Residual Deviance: 99.34 AIC: 285.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 102.5
## Residual Deviance: 98.8 AIC: 284.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 287.2897
##
## $true_model_parameters$AICc
## [1] 287.5424
##
## $true_model_parameters$BIC
## [1] 295.0751
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 97
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001b948958>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T4
## 2.7062 0.2031
##
## Degrees of Freedom: 100 Total (i.e. Null); 99 Residual
## (13 observations deleted due to missingness)
## Null Deviance: 112.3
## Residual Deviance: 104.1 AIC: 295.7
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T4
## 4.059 -0.231
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## (10 observations deleted due to missingness)
## Null Deviance: 107.4
## Residual Deviance: 103.8 AIC: 301
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.864
##
## Degrees of Freedom: 105 Total (i.e. Null); 105 Residual
## (8 observations deleted due to missingness)
## Null Deviance: 119.1
## Residual Deviance: 119.1 AIC: 317.2
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity WEAK "287.59" "-3" ""
## G + E only "287.8" NA ""
## Differential susceptibility STRONG "288.6" "10.78" "( 9.58 / 11.99 )"
## Diathesis-stress STRONG "288.73" "3" ""
## Differential susceptibility WEAK "291.97" "-5.96" "( -7.07 / -4.85 )"
## Diathesis-stress WEAK "292.09" "3" ""
## Vantage sensitivity STRONG "295.08" "-3" ""
## E only "303.51" NA ""
## G only "308.92" NA ""
## Intercept only "322.48" NA ""
## Within observable range?
## Vantage sensitivity WEAK ""
## G + E only ""
## Differential susceptibility STRONG "No"
## Diathesis-stress STRONG ""
## Differential susceptibility WEAK "No"
## Diathesis-stress WEAK ""
## Vantage sensitivity STRONG ""
## E only ""
## G only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity WEAK "0"
## G + E only NA
## Differential susceptibility STRONG "1"
## Diathesis-stress STRONG "0.868686868686869"
## Differential susceptibility WEAK "0"
## Diathesis-stress WEAK "0.868686868686869"
## Vantage sensitivity STRONG "0.0303030303030303"
## E only NA
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8888 -0.5629 -0.1444 0.5825 2.3176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.19774 0.28466 7.720 1.1e-11 ***
## E 0.63883 0.18101 3.529 0.000642 ***
## G:E -0.08784 0.03117 -2.818 0.005860 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9159551)
##
## Null deviance: 100.582 on 98 degrees of freedom
## Residual deviance: 87.932 on 96 degrees of freedom
## AIC: 277.21
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8888 -0.5629 -0.1444 0.5825 2.3176
##
## Coefficients: (-2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T4 1.00000 0.05015 19.94 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9159551)
##
## Null deviance: 100.582 on 99 degrees of freedom
## Residual deviance: 87.932 on 96 degrees of freedom
## AIC: 277.21
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8888 -0.5509 -0.1357 0.5825 2.3176
##
## Coefficients: (-2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T4 1.000 0.309 3.236 0.00166 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9158518)
##
## Null deviance: 100.582 on 99 degrees of freedom
## Residual deviance: 87.922 on 96 degrees of freedom
## AIC: 277.21
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_onemonth", drop=FALSE], env=df[,"ev_onemonth", drop = FALSE], formula_noGxE = wb_onemonth ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.45817 0.05274
##
## Degrees of Freedom: 78 Total (i.e. Null); 77 Residual
## Null Deviance: 51.04
## Residual Deviance: 44.92 AIC: 185.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 74.99
## Residual Deviance: 44.92 AIC: 183.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 54.83
## Residual Deviance: 44.92 AIC: 183.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 185.5997
##
## $true_model_parameters$AICc
## [1] 185.9197
##
## $true_model_parameters$BIC
## [1] 192.7081
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 77
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G - E
## <environment: 0x000000001c74e098>
##
## $crossover
## [1] 2.875
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.41797 -0.24573 0.09639
##
## Degrees of Freedom: 78 Total (i.e. Null); 76 Residual
## Null Deviance: 51.04
## Residual Deviance: 44.59 AIC: 187
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 145
## Residual Deviance: 44.59 AIC: 183
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 55.01
## Residual Deviance: 44.58 AIC: 183
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 187.0167
##
## $true_model_parameters$AICc
## [1] 187.5572
##
## $true_model_parameters$BIC
## [1] 196.4945
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 76
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G
## <environment: 0x000000001bc0cfe0>
##
## $crossover
## [1] 2.875
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.39658 0.03965
##
## Degrees of Freedom: 78 Total (i.e. Null); 77 Residual
## Null Deviance: 51.04
## Residual Deviance: 47.27 AIC: 189.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 71.67
## Residual Deviance: 47.27 AIC: 187.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 54.26
## Residual Deviance: 47.22 AIC: 187.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 189.6217
##
## $true_model_parameters$AICc
## [1] 189.9417
##
## $true_model_parameters$BIC
## [1] 196.73
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 77
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G - E
## <environment: 0x000000001b088148>
##
## $crossover
## [1] -1.625
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.68529 0.05152
##
## Degrees of Freedom: 78 Total (i.e. Null); 77 Residual
## Null Deviance: 51.04
## Residual Deviance: 44.85 AIC: 185.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 98.82
## Residual Deviance: 44.85 AIC: 183.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_onemonth
## 3.773 1.000
##
## Degrees of Freedom: 79 Total (i.e. Null); 77 Residual
## Null Deviance: 98.82
## Residual Deviance: 44.85 AIC: 185.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 187.4712
##
## $true_model_parameters$AICc
## [1] 188.0117
##
## $true_model_parameters$BIC
## [1] 196.9489
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 76
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G - E
## <environment: 0x000000001a7dd0f0>
##
## $crossover
## [1] 3.773169
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_onemonth ev_onemonth
## 3.4163 -0.1412 0.2601
##
## Degrees of Freedom: 78 Total (i.e. Null); 76 Residual
## (35 observations deleted due to missingness)
## Null Deviance: 51.04
## Residual Deviance: 45.38 AIC: 188.4
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.26155 0.38840 -0.02469
##
## Degrees of Freedom: 78 Total (i.e. Null); 76 Residual
## Null Deviance: 51.04
## Residual Deviance: 45.91 AIC: 189.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 55.37
## Residual Deviance: 45.91 AIC: 185.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 54.65
## Residual Deviance: 45.91 AIC: 185.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 189.3168
##
## $true_model_parameters$AICc
## [1] 189.8573
##
## $true_model_parameters$BIC
## [1] 198.7946
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 76
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G
## <environment: 0x000000001a77a768>
##
## $crossover
## [1] -1.625
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.1659 -0.5786 0.1596
##
## Degrees of Freedom: 78 Total (i.e. Null); 76 Residual
## Null Deviance: 51.04
## Residual Deviance: 44.39 AIC: 186.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_onemonth
## 1
##
## Degrees of Freedom: 79 Total (i.e. Null); 78 Residual
## Null Deviance: 157.7
## Residual Deviance: 44.39 AIC: 182.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_onemonth
## 1.909 1.000
##
## Degrees of Freedom: 79 Total (i.e. Null); 77 Residual
## Null Deviance: 56.31
## Residual Deviance: 44.39 AIC: 184.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 188.6542
##
## $true_model_parameters$AICc
## [1] 189.4761
##
## $true_model_parameters$BIC
## [1] 200.5014
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 75
##
## $true_model_parameters$null.deviance
## [1] 51.04044
##
##
## $ylim
## NULL
##
## $formula
## wb_onemonth ~ 1 + G * E - G
## <environment: 0x0000000017e9ef20>
##
## $crossover
## [1] 1.909142
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_onemonth
## 2.6818 0.2749
##
## Degrees of Freedom: 81 Total (i.e. Null); 80 Residual
## (32 observations deleted due to missingness)
## Null Deviance: 53.64
## Residual Deviance: 47.87 AIC: 194.6
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_onemonth
## 3.6881 -0.1496
##
## Degrees of Freedom: 83 Total (i.e. Null); 82 Residual
## (30 observations deleted due to missingness)
## Null Deviance: 52.99
## Residual Deviance: 52.12 AIC: 204.3
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.923
##
## Degrees of Freedom: 87 Total (i.e. Null); 87 Residual
## (26 observations deleted due to missingness)
## Null Deviance: 55.8
## Residual Deviance: 55.8 AIC: 213.6
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "192.71" "2.88" ""
## Diathesis-stress WEAK "196.49" "2.87" ""
## Vantage sensitivity STRONG "196.73" "-1.63" ""
## Differential susceptibility STRONG "196.95" "3.77" "( 2.94 / 4.6 )"
## G + E only "197.87" NA ""
## Vantage sensitivity WEAK "198.79" "-1.62" ""
## Differential susceptibility WEAK "200.5" "1.91" "( 1.08 / 2.73 )"
## E only "201.79" NA ""
## G only "211.58" NA ""
## Intercept only "218.6" NA ""
## Within observable range?
## Diathesis-stress STRONG ""
## Diathesis-stress WEAK ""
## Vantage sensitivity STRONG ""
## Differential susceptibility STRONG "No"
## G + E only ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "Yes"
## E only ""
## G only ""
## Intercept only ""
## % of observations below crossover
## Diathesis-stress STRONG "1"
## Diathesis-stress WEAK "1"
## Vantage sensitivity STRONG "0"
## Differential susceptibility STRONG "1"
## G + E only NA
## Vantage sensitivity WEAK "0.0126582278481013"
## Differential susceptibility WEAK "0.873417721518987"
## E only NA
## G only NA
## Intercept only NA
##
## $E_range
## [1] -1.625 2.875
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7250 -0.5312 -0.1335 0.4739 1.7612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.45817 0.19053 18.150 < 2e-16 ***
## G:E 0.05274 0.01629 3.238 0.00178 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.583436)
##
## Null deviance: 51.040 on 78 degrees of freedom
## Residual deviance: 44.925 on 77 degrees of freedom
## AIC: 185.6
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7250 -0.5312 -0.1335 0.4739 1.7612
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_onemonth 1.0000 0.1393 7.178 3.81e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.583436)
##
## Null deviance: 51.040 on 79 degrees of freedom
## Residual deviance: 44.925 on 77 degrees of freedom
## AIC: 185.6
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7164 -0.5126 -0.1209 0.4796 1.7620
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_onemonth 1.0000 0.2426 4.122 9.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.5833644)
##
## Null deviance: 51.040 on 79 degrees of freedom
## Residual deviance: 44.919 on 77 degrees of freedom
## AIC: 185.6
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T1", drop=FALSE], env=df[,"ev_T1", drop = FALSE], formula_noGxE = wb_T2 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.23329 0.02584
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 76.86 AIC: 258.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 87.76
## Residual Deviance: 76.86 AIC: 256.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 82.19
## Residual Deviance: 76.86 AIC: 256.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 258.6969
##
## $true_model_parameters$AICc
## [1] 258.955
##
## $true_model_parameters$BIC
## [1] 266.4211
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001c08f680>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.5538 0.0209
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 78.15 AIC: 260.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 97.72
## Residual Deviance: 78.15 AIC: 258.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 82.69
## Residual Deviance: 78.08 AIC: 258.2
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 260.3175
##
## $true_model_parameters$AICc
## [1] 260.5755
##
## $true_model_parameters$BIC
## [1] 268.0416
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001ab451e8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T1
## 2.8403 0.1345
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 77 AIC: 260.5
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.972
##
## Degrees of Freedom: 98 Total (i.e. Null); 98 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 81.04 AIC: 265.1
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T1 ev_T1
## 3.11304 -0.05468 0.13309
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## (17 observations deleted due to missingness)
## Null Deviance: 80.99
## Residual Deviance: 76.76 AIC: 260.6
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.43476 0.19040 -0.01124
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 76.77 AIC: 260.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 82.43
## Residual Deviance: 76.77 AIC: 256.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 82.47
## Residual Deviance: 76.77 AIC: 256.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 260.5904
##
## $true_model_parameters$AICc
## [1] 261.0252
##
## $true_model_parameters$BIC
## [1] 270.8893
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x0000000013eb3b80>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.23942 0.04910 0.01674
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 76.83 AIC: 260.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 81.41
## Residual Deviance: 76.83 AIC: 256.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 82.33
## Residual Deviance: 76.83 AIC: 256.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 260.6654
##
## $true_model_parameters$AICc
## [1] 261.1002
##
## $true_model_parameters$BIC
## [1] 270.9642
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x000000001b80efa8>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.28927 0.02559
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 76.85 AIC: 258.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 90.93
## Residual Deviance: 76.85 AIC: 256.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## 3.456 1.000
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 90.93
## Residual Deviance: 76.85 AIC: 258.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 260.6883
##
## $true_model_parameters$AICc
## [1] 261.1231
##
## $true_model_parameters$BIC
## [1] 270.9872
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x0000000012ea8170>
##
## $crossover
## [1] 3.456072
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T1
## 3.34520 -0.07507
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 80.99
## Residual Deviance: 80.68 AIC: 265.1
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## -9.224019 0.136114 -0.000595
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 76.76 AIC: 260.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 84.16
## Residual Deviance: 76.76 AIC: 256.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## -90.61 1.00
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 14500
## Residual Deviance: 76.76 AIC: 258.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 262.5684
##
## $true_model_parameters$AICc
## [1] 263.2277
##
## $true_model_parameters$BIC
## [1] 275.4419
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 93
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x0000000018869560>
##
## $crossover
## [1] -90.6085
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "266.42" "3" ""
## Vantage sensitivity STRONG "268.04" "-3" ""
## E only "268.23" NA ""
## Intercept only "270.32" NA ""
## G + E only "270.87" NA ""
## Vantage sensitivity WEAK "270.89" "-3" ""
## Diathesis-stress WEAK "270.96" "3" ""
## Differential susceptibility STRONG "270.99" "3.46" "( 1.83 / 5.08 )"
## G only "272.81" NA ""
## Differential susceptibility WEAK "275.44" "-90.61" "( -92.21 / -89.01 )"
## Within observable range?
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## E only ""
## Intercept only ""
## G + E only ""
## Vantage sensitivity WEAK ""
## Diathesis-stress WEAK ""
## Differential susceptibility STRONG "No"
## G only ""
## Differential susceptibility WEAK "No"
## % of observations below crossover
## Diathesis-stress STRONG "1"
## Vantage sensitivity STRONG "0.0103092783505155"
## E only NA
## Intercept only NA
## G + E only NA
## Vantage sensitivity WEAK "0.0103092783505155"
## Diathesis-stress WEAK "1"
## Differential susceptibility STRONG "1"
## G only NA
## Differential susceptibility WEAK "0"
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.23329 -0.57665 -0.00329 0.52958 2.11020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23329 0.14838 21.790 <2e-16 ***
## G:E 0.02584 0.01144 2.259 0.0262 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.8090294)
##
## Null deviance: 80.987 on 96 degrees of freedom
## Residual deviance: 76.858 on 95 degrees of freedom
## AIC: 258.7
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.23329 -0.57665 -0.00329 0.52958 2.11020
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T1 1.0000 0.2724 3.671 0.000399 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.8090294)
##
## Null deviance: 80.987 on 97 degrees of freedom
## Residual deviance: 76.858 on 95 degrees of freedom
## AIC: 258.7
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.23037 -0.57428 -0.00233 0.53018 2.11077
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T1 1.0000 0.3895 2.567 0.0118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.8090254)
##
## Null deviance: 80.987 on 97 degrees of freedom
## Residual deviance: 76.857 on 95 degrees of freedom
## AIC: 258.7
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T2", drop=FALSE], env=df[,"ev_T2", drop = FALSE], formula_noGxE = wb_T3 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.23991 0.02864
##
## Degrees of Freedom: 86 Total (i.e. Null); 85 Residual
## Null Deviance: 69.83
## Residual Deviance: 63.71 AIC: 225.8
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 79.95
## Residual Deviance: 63.71 AIC: 223.8
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 70.35
## Residual Deviance: 63.71 AIC: 223.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 225.7934
##
## $true_model_parameters$AICc
## [1] 226.0826
##
## $true_model_parameters$BIC
## [1] 233.1911
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 85
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001bde5a28>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.40765 0.02597
##
## Degrees of Freedom: 86 Total (i.e. Null); 85 Residual
## Null Deviance: 69.83
## Residual Deviance: 64.92 AIC: 227.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 90.82
## Residual Deviance: 64.92 AIC: 225.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 71.48
## Residual Deviance: 64.86 AIC: 225.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 227.4324
##
## $true_model_parameters$AICc
## [1] 227.7216
##
## $true_model_parameters$BIC
## [1] 234.8302
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 85
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001a951530>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.21285 -0.13246 0.05193
##
## Degrees of Freedom: 86 Total (i.e. Null); 84 Residual
## Null Deviance: 69.83
## Residual Deviance: 63.5 AIC: 227.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 116.9
## Residual Deviance: 63.5 AIC: 223.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 70.38
## Residual Deviance: 63.47 AIC: 223.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 227.5046
##
## $true_model_parameters$AICc
## [1] 227.9924
##
## $true_model_parameters$BIC
## [1] 237.3682
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 84
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001b38a2e8>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.19621 0.02883
##
## Degrees of Freedom: 86 Total (i.e. Null); 85 Residual
## Null Deviance: 69.83
## Residual Deviance: 63.71 AIC: 225.8
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 77.52
## Residual Deviance: 63.71 AIC: 223.8
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## 2.69 1.00
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## Null Deviance: 77.52
## Residual Deviance: 63.71 AIC: 225.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 227.7886
##
## $true_model_parameters$AICc
## [1] 228.2764
##
## $true_model_parameters$BIC
## [1] 237.6522
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 84
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x0000000013ac98b0>
##
## $crossover
## [1] 2.689635
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T2
## 2.8110 0.1448
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## (26 observations deleted due to missingness)
## Null Deviance: 71.03
## Residual Deviance: 65.93 AIC: 230.3
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T2 ev_T2
## 3.10480 -0.06002 0.14602
##
## Degrees of Freedom: 86 Total (i.e. Null); 84 Residual
## (27 observations deleted due to missingness)
## Null Deviance: 69.83
## Residual Deviance: 64.33 AIC: 228.6
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.351255 0.132347 0.003054
##
## Degrees of Freedom: 86 Total (i.e. Null); 84 Residual
## Null Deviance: 69.83
## Residual Deviance: 64.49 AIC: 228.9
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 64.85
## Residual Deviance: 64.49 AIC: 224.9
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 70.84
## Residual Deviance: 64.49 AIC: 224.9
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 228.8512
##
## $true_model_parameters$AICc
## [1] 229.339
##
## $true_model_parameters$BIC
## [1] 238.7148
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 84
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001246b4d8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.9707 -0.4573 0.1134
##
## Degrees of Freedom: 86 Total (i.e. Null); 84 Residual
## Null Deviance: 69.83
## Residual Deviance: 62.9 AIC: 226.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T2
## 1
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 160.9
## Residual Deviance: 62.9 AIC: 222.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## 1.184 1.000
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## Null Deviance: 70.28
## Residual Deviance: 62.9 AIC: 224.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 228.6799
##
## $true_model_parameters$AICc
## [1] 229.4206
##
## $true_model_parameters$BIC
## [1] 241.0094
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 83
##
## $true_model_parameters$null.deviance
## [1] 69.82989
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x0000000018d8bfb0>
##
## $crossover
## [1] 1.183544
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T2
## 3.37864 -0.08902
##
## Degrees of Freedom: 90 Total (i.e. Null); 89 Residual
## (23 observations deleted due to missingness)
## Null Deviance: 72.94
## Residual Deviance: 72.55 AIC: 243.6
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.899
##
## Degrees of Freedom: 104 Total (i.e. Null); 104 Residual
## (9 observations deleted due to missingness)
## Null Deviance: 91.69
## Residual Deviance: 91.69 AIC: 287.7
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "233.19" "3" ""
## Vantage sensitivity STRONG "234.83" "-3" ""
## Diathesis-stress WEAK "237.37" "3" ""
## Differential susceptibility STRONG "237.65" "2.69" "( 1.39 / 3.99 )"
## E only "237.76" NA ""
## G + E only "238.5" NA ""
## Vantage sensitivity WEAK "238.71" "-3" ""
## Differential susceptibility WEAK "241.01" "1.18" "( -0.07 / 2.44 )"
## G only "251.16" NA ""
## Intercept only "293.05" NA ""
## Within observable range?
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## Diathesis-stress WEAK ""
## Differential susceptibility STRONG "No"
## E only ""
## G + E only ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "Yes"
## G only ""
## Intercept only ""
## % of observations below crossover
## Diathesis-stress STRONG "0.873563218390805"
## Vantage sensitivity STRONG "0.0344827586206897"
## Diathesis-stress WEAK "0.873563218390805"
## Differential susceptibility STRONG "0.873563218390805"
## E only NA
## G + E only NA
## Vantage sensitivity WEAK "0"
## Differential susceptibility WEAK "0.632183908045977"
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2843 -0.5543 -0.0910 0.5507 2.2584
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.23991 0.15123 21.424 < 2e-16 ***
## G:E 0.02864 0.01002 2.857 0.00538 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7495673)
##
## Null deviance: 69.830 on 86 degrees of freedom
## Residual deviance: 63.713 on 85 degrees of freedom
## AIC: 225.79
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2843 -0.5543 -0.0910 0.5507 2.2584
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T2 1.0000 0.2149 4.654 1.19e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7495673)
##
## Null deviance: 69.830 on 87 degrees of freedom
## Residual deviance: 63.713 on 85 degrees of freedom
## AIC: 225.79
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.28592 -0.55535 -0.09254 0.55052 2.25837
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T2 1.0000 0.3362 2.975 0.00382 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7495653)
##
## Null deviance: 69.830 on 87 degrees of freedom
## Residual deviance: 63.713 on 85 degrees of freedom
## AIC: 225.79
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"hsc_T3", drop=FALSE], env=df[,"ev_T3", drop = FALSE], formula_noGxE = wb_T4 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.31054 0.03548
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.2 AIC: 286.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 131.9
## Residual Deviance: 102.2 AIC: 284.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 112.9
## Residual Deviance: 102.2 AIC: 284.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 286.3151
##
## $true_model_parameters$AICc
## [1] 286.5732
##
## $true_model_parameters$BIC
## [1] 294.0392
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001bd78498>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.2241 0.0337
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 104.4 AIC: 288.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 152.7
## Residual Deviance: 104.4 AIC: 286.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 116.2
## Residual Deviance: 104.3 AIC: 286.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 288.416
##
## $true_model_parameters$AICc
## [1] 288.6741
##
## $true_model_parameters$BIC
## [1] 296.1402
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001a8a0ab8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T3
## 2.7414 0.1942
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 112.8
## Residual Deviance: 103 AIC: 289
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.26609 0.03575
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.2 AIC: 286.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 128.3
## Residual Deviance: 102.2 AIC: 284.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## 2.746 1.000
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 128.3
## Residual Deviance: 102.2 AIC: 286.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 288.3115
##
## $true_model_parameters$AICc
## [1] 288.7462
##
## $true_model_parameters$BIC
## [1] 298.6103
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x0000000013a4cce8>
##
## $crossover
## [1] 2.745783
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.308476 -0.008593 0.036915
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.2 AIC: 288.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 134.4
## Residual Deviance: 102.2 AIC: 284.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.2 AIC: 284.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 288.3142
##
## $true_model_parameters$AICc
## [1] 288.749
##
## $true_model_parameters$BIC
## [1] 298.613
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001b226718>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T3 ev_T3
## 3.11838 -0.07245 0.19387
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## (17 observations deleted due to missingness)
## Null Deviance: 112.8
## Residual Deviance: 102.5 AIC: 288.6
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.132789 0.235887 -0.006894
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.7 AIC: 288.8
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 104.8
## Residual Deviance: 102.7 AIC: 284.8
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 114.2
## Residual Deviance: 102.7 AIC: 284.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 288.8429
##
## $true_model_parameters$AICc
## [1] 289.2777
##
## $true_model_parameters$BIC
## [1] 299.1417
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x00000000125b9388>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.19172 -0.04837 0.04440
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.2 AIC: 288.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## hsc_T3
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 134.3
## Residual Deviance: 102.2 AIC: 284.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## 2.365 1.000
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 123.1
## Residual Deviance: 102.2 AIC: 286.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 290.2987
##
## $true_model_parameters$AICc
## [1] 290.958
##
## $true_model_parameters$BIC
## [1] 303.1722
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 93
##
## $true_model_parameters$null.deviance
## [1] 112.7777
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x0000000018db3450>
##
## $crossover
## [1] 2.365059
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) hsc_T3
## 3.6182 -0.1427
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 113.1
## Residual Deviance: 112 AIC: 299.2
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.864
##
## Degrees of Freedom: 105 Total (i.e. Null); 105 Residual
## (8 observations deleted due to missingness)
## Null Deviance: 119.1
## Residual Deviance: 119.1 AIC: 317.2
##
##
## $results
## BIC crossover crossover 95%
## Diathesis-stress STRONG "294.04" "3" ""
## Vantage sensitivity STRONG "296.14" "-3" ""
## E only "296.73" NA ""
## Differential susceptibility STRONG "298.61" "2.75" "( 1.6 / 3.9 )"
## Diathesis-stress WEAK "298.61" "3" ""
## G + E only "298.93" NA ""
## Vantage sensitivity WEAK "299.14" "-3" ""
## Differential susceptibility WEAK "303.17" "2.37" "( 1.21 / 3.52 )"
## G only "306.95" NA ""
## Intercept only "322.48" NA ""
## Within observable range?
## Diathesis-stress STRONG ""
## Vantage sensitivity STRONG ""
## E only ""
## Differential susceptibility STRONG "No"
## Diathesis-stress WEAK ""
## G + E only ""
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## G only ""
## Intercept only ""
## % of observations below crossover
## Diathesis-stress STRONG "1"
## Vantage sensitivity STRONG "0"
## E only NA
## Differential susceptibility STRONG "0.855670103092783"
## Diathesis-stress WEAK "0.855670103092783"
## G + E only NA
## Vantage sensitivity WEAK "0.0618556701030928"
## Differential susceptibility WEAK "0.804123711340206"
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5105 -0.7105 -0.1600 0.7477 2.1876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.31054 0.17645 18.76 < 2e-16 ***
## G:E 0.03548 0.01130 3.14 0.00225 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.075519)
##
## Null deviance: 112.78 on 96 degrees of freedom
## Residual deviance: 102.17 on 95 degrees of freedom
## AIC: 286.32
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5105 -0.7105 -0.1600 0.7477 2.1876
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## hsc_T3 1.0000 0.1901 5.262 8.82e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.075519)
##
## Null deviance: 112.78 on 97 degrees of freedom
## Residual deviance: 102.17 on 95 degrees of freedom
## AIC: 286.32
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5131 -0.7130 -0.1597 0.7473 2.1872
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T3 1.0000 0.3168 3.156 0.00214 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.075517)
##
## Null deviance: 112.78 on 97 degrees of freedom
## Residual deviance: 102.17 on 95 degrees of freedom
## AIC: 286.32
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T1", drop=FALSE], env=df[,"ev_T1", drop = FALSE], formula_noGxE = wb_T1 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.05034 0.03127
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 68.01 AIC: 266.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 129.8
## Residual Deviance: 68.01 AIC: 264.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 79.07
## Residual Deviance: 68.01 AIC: 264.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 266.6308
##
## $true_model_parameters$AICc
## [1] 266.8551
##
## $true_model_parameters$BIC
## [1] 274.7594
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 109
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x000000001a9bec48>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T1
## 2.5927 0.1765
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 78.04
## Residual Deviance: 69.24 AIC: 270
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.08856 0.02803
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 70.52 AIC: 270.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 92.03
## Residual Deviance: 70.52 AIC: 268.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 81.66
## Residual Deviance: 70.43 AIC: 268.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 270.6548
##
## $true_model_parameters$AICc
## [1] 270.8791
##
## $true_model_parameters$BIC
## [1] 278.7834
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 109
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x000000001bf5dca0>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T1 ev_T1
## 1.5527 0.1812 0.1746
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## (3 observations deleted due to missingness)
## Null Deviance: 78.03
## Residual Deviance: 67.73 AIC: 268.2
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 1.71067 0.03006
##
## Degrees of Freedom: 110 Total (i.e. Null); 109 Residual
## Null Deviance: 78.03
## Residual Deviance: 67.84 AIC: 266.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 194.1
## Residual Deviance: 67.84 AIC: 264.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## -5.118 1.000
##
## Degrees of Freedom: 111 Total (i.e. Null); 109 Residual
## Null Deviance: 194.1
## Residual Deviance: 67.84 AIC: 266.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 268.3529
##
## $true_model_parameters$AICc
## [1] 268.7303
##
## $true_model_parameters$BIC
## [1] 279.1911
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G - E
## <environment: 0x000000001a4b5e68>
##
## $crossover
## [1] -5.11752
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.06742 -0.05534 0.04010
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 67.94 AIC: 268.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 169.6
## Residual Deviance: 67.94 AIC: 264.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 78.75
## Residual Deviance: 67.94 AIC: 264.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 268.5203
##
## $true_model_parameters$AICc
## [1] 268.8976
##
## $true_model_parameters$BIC
## [1] 279.3584
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x000000001240ce08>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.11986 0.51054 -0.05843
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 68.21 AIC: 269
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 161.6
## Residual Deviance: 68.21 AIC: 265
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 78.44
## Residual Deviance: 68.19 AIC: 264.9
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 268.9504
##
## $true_model_parameters$AICc
## [1] 269.3278
##
## $true_model_parameters$BIC
## [1] 279.7885
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 108
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x000000001b5c93d0>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 14.671016 0.189811 -0.002654
##
## Degrees of Freedom: 110 Total (i.e. Null); 108 Residual
## Null Deviance: 78.03
## Residual Deviance: 67.73 AIC: 268.2
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## Null Deviance: 190.3
## Residual Deviance: 67.73 AIC: 264.2
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## 69.19 1.00
##
## Degrees of Freedom: 111 Total (i.e. Null); 109 Residual
## Null Deviance: 15900
## Residual Deviance: 67.73 AIC: 266.2
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 270.1735
##
## $true_model_parameters$AICc
## [1] 270.745
##
## $true_model_parameters$BIC
## [1] 283.7212
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 107
##
## $true_model_parameters$null.deviance
## [1] 78.02667
##
##
## $ylim
## NULL
##
## $formula
## wb_T1 ~ 1 + G * E - G
## <environment: 0x000000001867be00>
##
## $crossover
## [1] 69.18552
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.735
##
## Degrees of Freedom: 112 Total (i.e. Null); 112 Residual
## (1 observation deleted due to missingness)
## Null Deviance: 78.12
## Residual Deviance: 78.12 AIC: 283
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T1
## 1.6193 0.1939
##
## Degrees of Freedom: 111 Total (i.e. Null); 110 Residual
## (2 observations deleted due to missingness)
## Null Deviance: 78.1
## Residual Deviance: 76.36 AIC: 280.9
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "274.76" "-3" ""
## E only "278.13" NA ""
## Diathesis-stress STRONG "278.78" "3" ""
## G + E only "279.01" NA ""
## Differential susceptibility STRONG "279.19" "-5.12" "( -6.06 / -4.17 )"
## Vantage sensitivity WEAK "279.36" "-3" ""
## Diathesis-stress WEAK "279.79" "3" ""
## Differential susceptibility WEAK "283.72" "69.19" "( 68.25 / 70.12 )"
## Intercept only "288.42" NA ""
## G only "289.1" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## E only ""
## Diathesis-stress STRONG ""
## G + E only ""
## Differential susceptibility STRONG "No"
## Vantage sensitivity WEAK ""
## Diathesis-stress WEAK ""
## Differential susceptibility WEAK "No"
## Intercept only ""
## G only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## E only NA
## Diathesis-stress STRONG "1"
## G + E only NA
## Differential susceptibility STRONG "0"
## Vantage sensitivity WEAK "0"
## Diathesis-stress WEAK "0.828828828828829"
## Differential susceptibility WEAK "1"
## Intercept only NA
## G only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.81121 -0.51123 -0.03923 0.57671 1.70342
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.050339 0.186237 11.009 < 2e-16 ***
## G:E 0.031269 0.007805 4.006 0.000113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6239599)
##
## Null deviance: 78.027 on 110 degrees of freedom
## Residual deviance: 68.012 on 109 degrees of freedom
## AIC: 266.63
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.81121 -0.51123 -0.03923 0.57671 1.70342
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T1 1.0000 0.1005 9.952 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.6239599)
##
## Null deviance: 78.027 on 111 degrees of freedom
## Residual deviance: 68.012 on 109 degrees of freedom
## AIC: 266.63
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.79977 -0.51026 -0.02948 0.57476 1.69807
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T1 1.0000 0.2375 4.21 5.26e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.62391)
##
## Null deviance: 78.027 on 111 degrees of freedom
## Residual deviance: 68.006 on 109 degrees of freedom
## AIC: 266.63
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits$vantage_sensitivity_STRONG, xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T2", drop=FALSE], env=df[,"ev_T2", drop = FALSE], formula_noGxE = wb_T2 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.26352 0.03282
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 79.46
## Residual Deviance: 69.64 AIC: 244.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 126.1
## Residual Deviance: 69.64 AIC: 242.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 80.32
## Residual Deviance: 69.63 AIC: 242.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 244.5602
##
## $true_model_parameters$AICc
## [1] 244.8268
##
## $true_model_parameters$BIC
## [1] 252.1901
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 92
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001ad1f188>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T2
## 2.8339 0.1752
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## (20 observations deleted due to missingness)
## Null Deviance: 79.46
## Residual Deviance: 71.7 AIC: 247.3
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.33529 0.02899
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 79.46
## Residual Deviance: 72.19 AIC: 247.9
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 92.14
## Residual Deviance: 72.19 AIC: 245.9
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 82.77
## Residual Deviance: 72.08 AIC: 245.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 247.9408
##
## $true_model_parameters$AICc
## [1] 248.2075
##
## $true_model_parameters$BIC
## [1] 255.5707
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 92
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001c542d38>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.32595 -0.18904 0.06308
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## Null Deviance: 79.46
## Residual Deviance: 68.95 AIC: 245.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 277.6
## Residual Deviance: 68.95 AIC: 241.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 79.85
## Residual Deviance: 68.92 AIC: 241.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 245.6309
##
## $true_model_parameters$AICc
## [1] 246.0803
##
## $true_model_parameters$BIC
## [1] 255.804
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 91
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x00000000138af740>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.04579 0.03197
##
## Degrees of Freedom: 93 Total (i.e. Null); 92 Residual
## Null Deviance: 79.46
## Residual Deviance: 69.56 AIC: 244.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 159.4
## Residual Deviance: 69.56 AIC: 242.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## -4.298 1.000
##
## Degrees of Freedom: 94 Total (i.e. Null); 92 Residual
## Null Deviance: 159.4
## Residual Deviance: 69.56 AIC: 244.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 246.4536
##
## $true_model_parameters$AICc
## [1] 246.903
##
## $true_model_parameters$BIC
## [1] 256.6267
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 91
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001a927ed0>
##
## $crossover
## [1] -4.298077
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T2 ev_T2
## 1.9923 0.1483 0.1728
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## (20 observations deleted due to missingness)
## Null Deviance: 79.46
## Residual Deviance: 70.61 AIC: 247.9
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.6962 -0.5791 0.1314
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## Null Deviance: 79.46
## Residual Deviance: 68.08 AIC: 244.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 348
## Residual Deviance: 68.08 AIC: 240.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## -0.8138 1.0000
##
## Degrees of Freedom: 94 Total (i.e. Null); 92 Residual
## Null Deviance: 86.41
## Residual Deviance: 68.08 AIC: 242.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 246.4413
##
## $true_model_parameters$AICc
## [1] 247.1231
##
## $true_model_parameters$BIC
## [1] 259.1577
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 90
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x0000000017d41538>
##
## $crossover
## [1] -0.8138428
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.359497 0.181217 -0.001061
##
## Degrees of Freedom: 93 Total (i.e. Null); 91 Residual
## Null Deviance: 79.46
## Residual Deviance: 71.7 AIC: 249.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 71.73
## Residual Deviance: 71.7 AIC: 245.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 94 Total (i.e. Null); 93 Residual
## Null Deviance: 81.1
## Residual Deviance: 71.7 AIC: 245.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 249.3088
##
## $true_model_parameters$AICc
## [1] 249.7582
##
## $true_model_parameters$BIC
## [1] 259.482
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 91
##
## $true_model_parameters$null.deviance
## [1] 79.46426
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x000000001b9bb938>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.972
##
## Degrees of Freedom: 98 Total (i.e. Null); 98 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 81.04 AIC: 265.1
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T2
## 2.1330 0.1474
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 79.86 AIC: 265.7
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "252.19" "-3" ""
## E only "254.94" NA ""
## Diathesis-stress STRONG "255.57" "3" ""
## Vantage sensitivity WEAK "255.8" "-3" ""
## Differential susceptibility STRONG "256.63" "-4.3" "( -5.36 / -3.24 )"
## G + E only "258.04" NA ""
## Differential susceptibility WEAK "259.16" "-0.81" "( -1.79 / 0.16 )"
## Diathesis-stress WEAK "259.48" "3" ""
## Intercept only "270.32" NA ""
## G only "273.47" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## E only ""
## Diathesis-stress STRONG ""
## Vantage sensitivity WEAK ""
## Differential susceptibility STRONG "No"
## G + E only ""
## Differential susceptibility WEAK "Yes"
## Diathesis-stress WEAK ""
## Intercept only ""
## G only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## E only NA
## Diathesis-stress STRONG "1"
## Vantage sensitivity WEAK "0.0212765957446809"
## Differential susceptibility STRONG "0"
## G + E only NA
## Differential susceptibility WEAK "0.159574468085106"
## Diathesis-stress WEAK "0.851063829787234"
## Intercept only NA
## G only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.64816 -0.60584 -0.02406 0.55261 2.26878
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.263517 0.215143 10.521 < 2e-16 ***
## G:E 0.032821 0.009109 3.603 0.00051 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7569208)
##
## Null deviance: 79.464 on 93 degrees of freedom
## Residual deviance: 69.637 on 92 degrees of freedom
## AIC: 244.56
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.64816 -0.60584 -0.02406 0.55261 2.26878
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T2 1.0000 0.1158 8.639 1.66e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7569208)
##
## Null deviance: 79.464 on 94 degrees of freedom
## Residual deviance: 69.637 on 92 degrees of freedom
## AIC: 244.56
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.64149 -0.60866 -0.01799 0.55855 2.26878
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T2 1.0000 0.2662 3.757 0.000301 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7568863)
##
## Null deviance: 79.464 on 94 degrees of freedom
## Residual deviance: 69.634 on 92 degrees of freedom
## AIC: 244.56
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T3", drop=FALSE], env=df[,"ev_T3", drop = FALSE], formula_noGxE = wb_T3 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.15319 0.03401
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 90.86
## Residual Deviance: 78.05 AIC: 269.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 146.7
## Residual Deviance: 78.05 AIC: 267.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 91.24
## Residual Deviance: 78.05 AIC: 267.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 269.7251
##
## $true_model_parameters$AICc
## [1] 269.9676
##
## $true_model_parameters$BIC
## [1] 277.6293
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 101
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001b3882e8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T3
## 2.7559 0.2036
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## (11 observations deleted due to missingness)
## Null Deviance: 90.86
## Residual Deviance: 79.67 AIC: 271.8
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.35089 0.03405
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 90.86
## Residual Deviance: 80.06 AIC: 272.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 112.8
## Residual Deviance: 80.06 AIC: 270.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 95.3
## Residual Deviance: 79.99 AIC: 270.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 272.3519
##
## $true_model_parameters$AICc
## [1] 272.5943
##
## $true_model_parameters$BIC
## [1] 280.2561
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 101
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x00000000186f6328>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.19991 -0.12267 0.05257
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## Null Deviance: 90.86
## Residual Deviance: 77.8 AIC: 271.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 241.8
## Residual Deviance: 77.8 AIC: 267.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 90.62
## Residual Deviance: 77.79 AIC: 267.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 271.397
##
## $true_model_parameters$AICc
## [1] 271.8052
##
## $true_model_parameters$BIC
## [1] 281.936
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 100
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001a901210>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.08818 0.03371
##
## Degrees of Freedom: 102 Total (i.e. Null); 101 Residual
## Null Deviance: 90.86
## Residual Deviance: 78.04 AIC: 269.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 157
## Residual Deviance: 78.04 AIC: 267.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## -3.362 1.000
##
## Degrees of Freedom: 103 Total (i.e. Null); 101 Residual
## Null Deviance: 157
## Residual Deviance: 78.04 AIC: 269.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 271.7178
##
## $true_model_parameters$AICc
## [1] 272.126
##
## $true_model_parameters$BIC
## [1] 282.2568
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 100
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001af67af8>
##
## $crossover
## [1] -3.362362
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T3 ev_T3
## 1.9639 0.1361 0.1933
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## (11 observations deleted due to missingness)
## Null Deviance: 90.86
## Residual Deviance: 78.88 AIC: 272.8
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.366822 0.189942 0.002372
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## Null Deviance: 90.86
## Residual Deviance: 79.66 AIC: 273.8
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 79.82
## Residual Deviance: 79.66 AIC: 269.8
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 92.85
## Residual Deviance: 79.66 AIC: 269.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 273.8406
##
## $true_model_parameters$AICc
## [1] 274.2488
##
## $true_model_parameters$BIC
## [1] 284.3795
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 100
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001beb3688>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.6304 -0.4780 0.1131
##
## Degrees of Freedom: 102 Total (i.e. Null); 100 Residual
## Null Deviance: 90.86
## Residual Deviance: 77.2 AIC: 270.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## Null Deviance: 282.8
## Residual Deviance: 77.2 AIC: 266.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## -0.6195 1.0000
##
## Degrees of Freedom: 103 Total (i.e. Null); 101 Residual
## Null Deviance: 97.76
## Residual Deviance: 77.2 AIC: 268.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 272.603
##
## $true_model_parameters$AICc
## [1] 273.2216
##
## $true_model_parameters$BIC
## [1] 285.7767
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 99
##
## $true_model_parameters$null.deviance
## [1] 90.85825
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x0000000012ea9130>
##
## $crossover
## [1] -0.6195284
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.899
##
## Degrees of Freedom: 104 Total (i.e. Null); 104 Residual
## (9 observations deleted due to missingness)
## Null Deviance: 91.69
## Residual Deviance: 91.69 AIC: 287.7
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T3
## 1.6587 0.2119
##
## Degrees of Freedom: 104 Total (i.e. Null); 103 Residual
## (9 observations deleted due to missingness)
## Null Deviance: 91.69
## Residual Deviance: 89.55 AIC: 287.3
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "277.63" "-3" ""
## E only "279.75" NA ""
## Diathesis-stress STRONG "280.26" "3" ""
## Vantage sensitivity WEAK "281.94" "-3" ""
## Differential susceptibility STRONG "282.26" "-3.36" "( -4.29 / -2.43 )"
## G + E only "283.36" NA ""
## Diathesis-stress WEAK "284.38" "3" ""
## Differential susceptibility WEAK "285.78" "-0.62" "( -1.57 / 0.33 )"
## Intercept only "293.05" NA ""
## G only "295.23" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## E only ""
## Diathesis-stress STRONG ""
## Vantage sensitivity WEAK ""
## Differential susceptibility STRONG "No"
## G + E only ""
## Diathesis-stress WEAK ""
## Differential susceptibility WEAK "Yes"
## Intercept only ""
## G only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## E only NA
## Diathesis-stress STRONG "0.854368932038835"
## Vantage sensitivity WEAK "0.0679611650485437"
## Differential susceptibility STRONG "0"
## G + E only NA
## Diathesis-stress WEAK "0.854368932038835"
## Differential susceptibility WEAK "0.135922330097087"
## Intercept only NA
## G only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.62872 -0.51442 -0.01613 0.52230 2.08558
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.153187 0.200457 10.741 < 2e-16 ***
## G:E 0.034014 0.008353 4.072 9.28e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7727262)
##
## Null deviance: 90.858 on 102 degrees of freedom
## Residual deviance: 78.045 on 101 degrees of freedom
## AIC: 269.73
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.62872 -0.51442 -0.01613 0.52230 2.08558
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T3 1.0000 0.1061 9.424 1.67e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7727262)
##
## Null deviance: 90.858 on 103 degrees of freedom
## Residual deviance: 78.045 on 101 degrees of freedom
## AIC: 269.73
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.62650 -0.51442 -0.01465 0.52434 2.08558
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T3 1.000 0.242 4.132 7.43e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7727246)
##
## Null deviance: 90.858 on 103 degrees of freedom
## Residual deviance: 78.045 on 101 degrees of freedom
## AIC: 269.73
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T4", drop=FALSE], env=df[,"ev_T4", drop = FALSE], formula_noGxE = wb_T4 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.26326 0.02742
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 94.74 AIC: 282.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 139.6
## Residual Deviance: 94.74 AIC: 280.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 101.3
## Residual Deviance: 94.73 AIC: 280.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 282.5964
##
## $true_model_parameters$AICc
## [1] 282.8491
##
## $true_model_parameters$BIC
## [1] 290.3818
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 97
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001c1668d0>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.17836 0.02327
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 97.02 AIC: 284.9
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 108.8
## Residual Deviance: 97.02 AIC: 282.9
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 103.7
## Residual Deviance: 96.91 AIC: 282.8
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 284.9489
##
## $true_model_parameters$AICc
## [1] 285.2015
##
## $true_model_parameters$BIC
## [1] 292.7342
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 97
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x0000000012581a20>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.22481 0.54785 -0.06727
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 93.28 AIC: 283.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 191.4
## Residual Deviance: 93.28 AIC: 279.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 99.93
## Residual Deviance: 93.28 AIC: 279.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 283.0528
##
## $true_model_parameters$AICc
## [1] 283.4783
##
## $true_model_parameters$BIC
## [1] 293.4332
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001a4bfe68>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T4 ev_T4
## 1.7356 0.1736 0.1550
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 100.6
## Residual Deviance: 93.87 AIC: 283.7
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 1.78548 0.02463
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 100.6
## Residual Deviance: 94.42 AIC: 282.3
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 221.6
## Residual Deviance: 94.42 AIC: 280.3
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T4
## -6.745 1.000
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 221.6
## Residual Deviance: 94.42 AIC: 282.3
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 284.2555
##
## $true_model_parameters$AICc
## [1] 284.681
##
## $true_model_parameters$BIC
## [1] 294.636
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001accb7b8>
##
## $crossover
## [1] -6.745432
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.260786 0.005736 0.026557
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 94.74 AIC: 284.6
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 136.8
## Residual Deviance: 94.74 AIC: 280.6
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 101.4
## Residual Deviance: 94.73 AIC: 280.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 284.5959
##
## $true_model_parameters$AICc
## [1] 285.0214
##
## $true_model_parameters$BIC
## [1] 294.9763
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001af56c08>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.2355 0.5403 -0.0660
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 100.6
## Residual Deviance: 93.27 AIC: 283.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T4
## 1
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## Null Deviance: 192.4
## Residual Deviance: 93.27 AIC: 279.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T4
## 3.072 1.000
##
## Degrees of Freedom: 99 Total (i.e. Null); 97 Residual
## Null Deviance: 112.3
## Residual Deviance: 93.27 AIC: 281.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 285.0524
##
## $true_model_parameters$AICc
## [1] 285.6976
##
## $true_model_parameters$BIC
## [1] 298.028
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 100.5818
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x00000000126212b8>
##
## $crossover
## [1] 3.072257
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T4
## 2.7062 0.2031
##
## Degrees of Freedom: 100 Total (i.e. Null); 99 Residual
## (13 observations deleted due to missingness)
## Null Deviance: 112.3
## Residual Deviance: 104.1 AIC: 295.7
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T4
## 1.6679 0.2071
##
## Degrees of Freedom: 103 Total (i.e. Null); 102 Residual
## (10 observations deleted due to missingness)
## Null Deviance: 107.4
## Residual Deviance: 104.5 AIC: 301.7
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.864
##
## Degrees of Freedom: 105 Total (i.e. Null); 105 Residual
## (8 observations deleted due to missingness)
## Null Deviance: 119.1
## Residual Deviance: 119.1 AIC: 317.2
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "290.38" "-3" ""
## Diathesis-stress STRONG "292.73" "3" ""
## Diathesis-stress WEAK "293.43" "3" ""
## G + E only "294.06" NA ""
## Differential susceptibility STRONG "294.64" "-6.75" "( -8.35 / -5.15 )"
## Vantage sensitivity WEAK "294.98" "-3" ""
## Differential susceptibility WEAK "298.03" "3.07" "( 1.71 / 4.43 )"
## E only "303.51" NA ""
## G only "309.62" NA ""
## Intercept only "322.48" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## Diathesis-stress STRONG ""
## Diathesis-stress WEAK ""
## G + E only ""
## Differential susceptibility STRONG "No"
## Vantage sensitivity WEAK ""
## Differential susceptibility WEAK "No"
## E only ""
## G only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0.0303030303030303"
## Diathesis-stress STRONG "0.868686868686869"
## Diathesis-stress WEAK "1"
## G + E only NA
## Differential susceptibility STRONG "0"
## Vantage sensitivity WEAK "0.0303030303030303"
## Differential susceptibility WEAK "1"
## E only NA
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.32699 -0.68355 -0.03445 0.62442 2.30488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.26326 0.27521 8.224 8.96e-13 ***
## G:E 0.02742 0.01121 2.445 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9767106)
##
## Null deviance: 100.582 on 98 degrees of freedom
## Residual deviance: 94.741 on 97 degrees of freedom
## AIC: 282.6
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.32699 -0.68355 -0.03445 0.62442 2.30488
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T4 1.0000 0.1476 6.776 9.61e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.9767106)
##
## Null deviance: 100.582 on 99 degrees of freedom
## Residual deviance: 94.741 on 97 degrees of freedom
## AIC: 282.6
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.31530 -0.67157 -0.02607 0.62922 2.30488
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T4 1.0000 0.3851 2.596 0.0109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.976589)
##
## Null deviance: 100.582 on 99 degrees of freedom
## Residual deviance: 94.729 on 97 degrees of freedom
## AIC: 282.6
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T1", drop=FALSE], env=df[,"ev_T1", drop = FALSE], formula_noGxE = wb_T2 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.37622 0.02581
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 75.54 AIC: 257
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 115.1
## Residual Deviance: 75.54 AIC: 255
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 81.84
## Residual Deviance: 75.51 AIC: 255
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 257.0136
##
## $true_model_parameters$AICc
## [1] 257.2717
##
## $true_model_parameters$BIC
## [1] 264.7378
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001b0a5cb0>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.20302 0.02002
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 77.89 AIC: 260
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 86.3
## Residual Deviance: 77.89 AIC: 258
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 83.51
## Residual Deviance: 77.78 AIC: 257.9
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 259.9909
##
## $true_model_parameters$AICc
## [1] 260.249
##
## $true_model_parameters$BIC
## [1] 267.715
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x000000001cb4b060>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T1
## 2.8403 0.1345
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 77 AIC: 260.5
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T1 ev_T1
## 1.5378 0.2247 0.1364
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## (17 observations deleted due to missingness)
## Null Deviance: 80.99
## Residual Deviance: 74.76 AIC: 258
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 1.68305 0.02315
##
## Degrees of Freedom: 96 Total (i.e. Null); 95 Residual
## Null Deviance: 80.99
## Residual Deviance: 74.87 AIC: 256.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 241.4
## Residual Deviance: 74.87 AIC: 254.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## -8.64 1.00
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 241.4
## Residual Deviance: 74.87 AIC: 256.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 258.1493
##
## $true_model_parameters$AICc
## [1] 258.5841
##
## $true_model_parameters$BIC
## [1] 268.4482
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G - E
## <environment: 0x0000000013ca8578>
##
## $crossover
## [1] -8.639728
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.42861 -0.13948 0.04769
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 75.12 AIC: 258.5
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 210.1
## Residual Deviance: 75.12 AIC: 254.5
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 80.87
## Residual Deviance: 75.11 AIC: 254.5
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 258.4746
##
## $true_model_parameters$AICc
## [1] 258.9094
##
## $true_model_parameters$BIC
## [1] 268.7735
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x00000000132a3010>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.24377 0.60269 -0.08073
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 75.31 AIC: 258.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 211.9
## Residual Deviance: 75.31 AIC: 254.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 80.68
## Residual Deviance: 75.24 AIC: 254.6
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 258.7229
##
## $true_model_parameters$AICc
## [1] 259.1577
##
## $true_model_parameters$BIC
## [1] 269.0217
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x000000001c534400>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.972
##
## Degrees of Freedom: 98 Total (i.e. Null); 98 Residual
## (15 observations deleted due to missingness)
## Null Deviance: 81.04
## Residual Deviance: 81.04 AIC: 265.1
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T1
## 1.7045 0.2187
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 80.99
## Residual Deviance: 78.92 AIC: 262.9
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 5.03333 0.22271 -0.01493
##
## Degrees of Freedom: 96 Total (i.e. Null); 94 Residual
## Null Deviance: 80.99
## Residual Deviance: 74.74 AIC: 258
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T1
## 1
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 243.8
## Residual Deviance: 74.74 AIC: 254
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T1
## 16.11 1.00
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 494.3
## Residual Deviance: 74.74 AIC: 256
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 259.9861
##
## $true_model_parameters$AICc
## [1] 260.6455
##
## $true_model_parameters$BIC
## [1] 272.8597
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 93
##
## $true_model_parameters$null.deviance
## [1] 80.98722
##
##
## $ylim
## NULL
##
## $formula
## wb_T2 ~ 1 + G * E - G
## <environment: 0x0000000019573220>
##
## $crossover
## [1] 16.10789
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "264.74" "-3" ""
## Diathesis-stress STRONG "267.72" "3" ""
## E only "268.23" NA ""
## G + E only "268.31" NA ""
## Differential susceptibility STRONG "268.45" "-8.64" "( -10.2 / -7.08 )"
## Vantage sensitivity WEAK "268.77" "-3" ""
## Diathesis-stress WEAK "269.02" "3" ""
## Intercept only "270.32" NA ""
## G only "270.65" NA ""
## Differential susceptibility WEAK "272.86" "16.11" "( 14.57 / 17.65 )"
## Within observable range?
## Vantage sensitivity STRONG ""
## Diathesis-stress STRONG ""
## E only ""
## G + E only ""
## Differential susceptibility STRONG "No"
## Vantage sensitivity WEAK ""
## Diathesis-stress WEAK ""
## Intercept only ""
## G only ""
## Differential susceptibility WEAK "No"
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## Diathesis-stress STRONG "0.804123711340206"
## E only NA
## G + E only NA
## Differential susceptibility STRONG "0"
## Vantage sensitivity WEAK "0"
## Diathesis-stress WEAK "0.804123711340206"
## Intercept only NA
## G only NA
## Differential susceptibility WEAK "1"
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.30531 -0.62788 -0.02788 0.52052 2.10439
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.376221 0.243841 9.745 5.79e-16 ***
## G:E 0.025808 0.009856 2.618 0.0103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7951111)
##
## Null deviance: 80.987 on 96 degrees of freedom
## Residual deviance: 75.536 on 95 degrees of freedom
## AIC: 257.01
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.30531 -0.62788 -0.02788 0.52052 2.10439
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T1 1.0000 0.1418 7.052 2.82e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7951111)
##
## Null deviance: 80.987 on 97 degrees of freedom
## Residual deviance: 75.536 on 95 degrees of freedom
## AIC: 257.01
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2793 -0.6062 -0.0166 0.5205 2.1086
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T1 1.0000 0.3544 2.822 0.00582 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.794877)
##
## Null deviance: 80.987 on 97 degrees of freedom
## Residual deviance: 75.513 on 95 degrees of freedom
## AIC: 257.01
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T2", drop=FALSE], env=df[,"ev_T2", drop = FALSE], formula_noGxE = wb_T3 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.33720 0.02716
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 71.03
## Residual Deviance: 64.74 AIC: 228.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 100
## Residual Deviance: 64.74 AIC: 226.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 71.31
## Residual Deviance: 64.74 AIC: 226.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 228.7266
##
## $true_model_parameters$AICc
## [1] 229.0124
##
## $true_model_parameters$BIC
## [1] 236.1586
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 86
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001a684fc0>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T2
## 2.8110 0.1448
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## (26 observations deleted due to missingness)
## Null Deviance: 71.03
## Residual Deviance: 65.93 AIC: 230.3
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.21779 0.02309
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 71.03
## Residual Deviance: 66.5 AIC: 231.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 79.29
## Residual Deviance: 66.5 AIC: 229.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 72.74
## Residual Deviance: 66.44 AIC: 229
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 231.0838
##
## $true_model_parameters$AICc
## [1] 231.3695
##
## $true_model_parameters$BIC
## [1] 238.5158
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 86
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x000000001ccf5078>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.3758 -0.1596 0.0532
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## Null Deviance: 71.03
## Residual Deviance: 64.33 AIC: 230.2
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 199.8
## Residual Deviance: 64.33 AIC: 226.2
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 70.92
## Residual Deviance: 64.33 AIC: 226.2
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 230.1672
##
## $true_model_parameters$AICc
## [1] 230.6492
##
## $true_model_parameters$BIC
## [1] 240.0766
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 85
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x00000000185d4610>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 1.87494 0.02607
##
## Degrees of Freedom: 87 Total (i.e. Null); 86 Residual
## Null Deviance: 71.03
## Residual Deviance: 64.5 AIC: 228.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 165.6
## Residual Deviance: 64.5 AIC: 226.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## -6.246 1.000
##
## Degrees of Freedom: 88 Total (i.e. Null); 86 Residual
## Null Deviance: 165.6
## Residual Deviance: 64.5 AIC: 228.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 230.3915
##
## $true_model_parameters$AICc
## [1] 230.8734
##
## $true_model_parameters$BIC
## [1] 240.3008
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 85
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G - E
## <environment: 0x00000000180ee550>
##
## $crossover
## [1] -6.245715
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T2 ev_T2
## 1.7796 0.1795 0.1474
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## (26 observations deleted due to missingness)
## Null Deviance: 71.03
## Residual Deviance: 64.78 AIC: 230.8
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.24908 0.31699 -0.02965
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## Null Deviance: 71.03
## Residual Deviance: 65.68 AIC: 232
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 86.77
## Residual Deviance: 65.68 AIC: 228
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 71.2
## Residual Deviance: 65.66 AIC: 228
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 231.9918
##
## $true_model_parameters$AICc
## [1] 232.4737
##
## $true_model_parameters$BIC
## [1] 241.9011
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 85
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001b7336b0>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.56336 -0.28664 0.07518
##
## Degrees of Freedom: 87 Total (i.e. Null); 85 Residual
## Null Deviance: 71.03
## Residual Deviance: 64.28 AIC: 230.1
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T2
## 1
##
## Degrees of Freedom: 88 Total (i.e. Null); 87 Residual
## Null Deviance: 207.1
## Residual Deviance: 64.28 AIC: 226.1
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T2
## -1.736 1.000
##
## Degrees of Freedom: 88 Total (i.e. Null); 86 Residual
## Null Deviance: 81.69
## Residual Deviance: 64.28 AIC: 228.1
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 232.0984
##
## $true_model_parameters$AICc
## [1] 232.8301
##
## $true_model_parameters$BIC
## [1] 244.4851
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 84
##
## $true_model_parameters$null.deviance
## [1] 71.02864
##
##
## $ylim
## NULL
##
## $formula
## wb_T3 ~ 1 + G * E - G
## <environment: 0x000000001c4718f0>
##
## $crossover
## [1] -1.735692
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T2
## 1.8768 0.1839
##
## Degrees of Freedom: 92 Total (i.e. Null); 91 Residual
## (21 observations deleted due to missingness)
## Null Deviance: 74.2
## Residual Deviance: 72.84 AIC: 247.2
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.899
##
## Degrees of Freedom: 104 Total (i.e. Null); 104 Residual
## (9 observations deleted due to missingness)
## Null Deviance: 91.69
## Residual Deviance: 91.69 AIC: 287.7
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "236.16" "-3" ""
## E only "237.76" NA ""
## Diathesis-stress STRONG "238.52" "3" ""
## Vantage sensitivity WEAK "240.08" "-3" ""
## Differential susceptibility STRONG "240.3" "-6.25" "( -7.54 / -4.95 )"
## G + E only "240.69" NA ""
## Diathesis-stress WEAK "241.9" "3" ""
## Differential susceptibility WEAK "244.49" "-1.74" "( -3.01 / -0.46 )"
## G only "254.8" NA ""
## Intercept only "293.05" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## E only ""
## Diathesis-stress STRONG ""
## Vantage sensitivity WEAK ""
## Differential susceptibility STRONG "No"
## G + E only ""
## Diathesis-stress WEAK ""
## Differential susceptibility WEAK "No"
## G only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## E only NA
## Diathesis-stress STRONG "1"
## Vantage sensitivity WEAK "0.0340909090909091"
## Differential susceptibility STRONG "0"
## G + E only NA
## Diathesis-stress WEAK "1"
## Differential susceptibility WEAK "0.102272727272727"
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3519 -0.5564 -0.1075 0.5793 2.1536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.33720 0.21920 10.663 < 2e-16 ***
## G:E 0.02716 0.00940 2.889 0.00489 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7528396)
##
## Null deviance: 71.029 on 87 degrees of freedom
## Residual deviance: 64.744 on 86 degrees of freedom
## AIC: 228.73
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3519 -0.5564 -0.1075 0.5793 2.1536
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T2 1.000 0.146 6.847 1.06e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7528396)
##
## Null deviance: 71.029 on 88 degrees of freedom
## Residual deviance: 64.744 on 86 degrees of freedom
## AIC: 228.73
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3409 -0.5404 -0.1072 0.5936 2.1536
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T2 1.0000 0.3384 2.955 0.00403 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.7527469)
##
## Null deviance: 71.029 on 88 degrees of freedom
## Residual deviance: 64.736 on 86 degrees of freedom
## AIC: 228.73
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")
GxE_test_BIC = GxE_interaction_test(data=df, genes=df[,"aes_T3", drop=FALSE], env=df[,"ev_T3", drop = FALSE], formula_noGxE = wb_T4 ~ 1, crossover = c("min","max"), criterion="BIC")
GxE_test_BIC
## $fits
## $fits$vantage_sensitivity_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.18623 0.03148
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.4 AIC: 288.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 157.8
## Residual Deviance: 102.4 AIC: 286.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 113.1
## Residual Deviance: 102.4 AIC: 286.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 288.4242
##
## $true_model_parameters$AICc
## [1] 288.6795
##
## $true_model_parameters$BIC
## [1] 296.1791
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x00000000122cdfd8>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_E_only
##
## Call: glm(formula = formula_model_E_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) ev_T3
## 2.7414 0.1942
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 112.8
## Residual Deviance: 103 AIC: 289
##
## $fits$diathesis_stress_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 3.29301 0.03123
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 112.8
## Residual Deviance: 104.1 AIC: 290
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 130.9
## Residual Deviance: 104.1 AIC: 288
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 116.2
## Residual Deviance: 104 AIC: 287.9
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 289.9889
##
## $true_model_parameters$AICc
## [1] 290.2442
##
## $true_model_parameters$BIC
## [1] 297.7438
##
## $true_model_parameters$rank
## [1] 2
##
## $true_model_parameters$df.residual
## [1] 96
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x000000001c0a08f8>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_GandE_only
##
## Call: glm(formula = formula_model_GandE_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T3 ev_T3
## 1.9973 0.1276 0.1853
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## (16 observations deleted due to missingness)
## Null Deviance: 112.8
## Residual Deviance: 102.4 AIC: 290.4
##
## $fits$diff_suscept_STRONG
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) G:E
## 2.07915 0.03102
##
## Degrees of Freedom: 97 Total (i.e. Null); 96 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.4 AIC: 288.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 173.1
## Residual Deviance: 102.4 AIC: 286.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## -3.642 1.000
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 173.1
## Residual Deviance: 102.4 AIC: 288.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 290.4115
##
## $true_model_parameters$AICc
## [1] 290.8416
##
## $true_model_parameters$BIC
## [1] 300.7513
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G - E
## <environment: 0x0000000018f69740>
##
## $crossover
## [1] -3.641813
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$vantage_sensitivity_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 2.185734 0.001494 0.031252
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.4 AIC: 290.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 157
## Residual Deviance: 102.4 AIC: 286.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 113.1
## Residual Deviance: 102.4 AIC: 286.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 290.4241
##
## $true_model_parameters$AICc
## [1] 290.8542
##
## $true_model_parameters$BIC
## [1] 300.764
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x0000000019379790>
##
## $crossover
## [1] -3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diathesis_stress_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 3.32046 0.36361 -0.02947
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.7 AIC: 290.7
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 126.6
## Residual Deviance: 102.7 AIC: 286.7
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## ev_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 113
## Residual Deviance: 102.7 AIC: 286.7
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 290.6713
##
## $true_model_parameters$AICc
## [1] 291.1014
##
## $true_model_parameters$BIC
## [1] 301.0112
##
## $true_model_parameters$rank
## [1] 3
##
## $true_model_parameters$df.residual
## [1] 95
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001aeab730>
##
## $crossover
## [1] 3
##
## $crossover_fixed
## [1] TRUE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$diff_suscept_WEAK
## $fit_main
##
## Call: stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Coefficients:
## (Intercept) E G:E
## 0.81421 0.11629 0.01166
##
## Degrees of Freedom: 97 Total (i.e. Null); 95 Residual
## Null Deviance: 112.8
## Residual Deviance: 102.3 AIC: 290.4
##
## $fit_genes
##
## Call: stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## aes_T3
## 1
##
## Degrees of Freedom: 98 Total (i.e. Null); 97 Residual
## Null Deviance: 161.3
## Residual Deviance: 102.3 AIC: 286.4
##
## $fit_env
##
## Call: stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Coefficients:
## crossover ev_T3
## -10.45 1.00
##
## Degrees of Freedom: 98 Total (i.e. Null); 96 Residual
## Null Deviance: 524.3
## Residual Deviance: 102.3 AIC: 288.4
##
## $true_model_parameters
## $true_model_parameters$AIC
## [1] 292.3663
##
## $true_model_parameters$AICc
## [1] 293.0185
##
## $true_model_parameters$BIC
## [1] 305.2912
##
## $true_model_parameters$rank
## [1] 4
##
## $true_model_parameters$df.residual
## [1] 94
##
## $true_model_parameters$null.deviance
## [1] 112.8478
##
##
## $ylim
## NULL
##
## $formula
## wb_T4 ~ 1 + G * E - G
## <environment: 0x000000001a444fc8>
##
## $crossover
## [1] -10.4487
##
## $crossover_fixed
## [1] FALSE
##
## $conv
## [1] TRUE
##
## attr(,"class")
## [1] "LEGIT"
##
## $fits$model_G_only
##
## Call: glm(formula = formula_model_G_only, family = family, data = cbind(data,
## genes, env))
##
## Coefficients:
## (Intercept) aes_T3
## 1.6253 0.2116
##
## Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
## (14 observations deleted due to missingness)
## Null Deviance: 113.2
## Residual Deviance: 111.3 AIC: 300.5
##
## $fits$model_intercept_only
##
## Call: glm(formula = formula_model_intercept_only, family = family,
## data = cbind(data, genes, env))
##
## Coefficients:
## (Intercept)
## 2.864
##
## Degrees of Freedom: 105 Total (i.e. Null); 105 Residual
## (8 observations deleted due to missingness)
## Null Deviance: 119.1
## Residual Deviance: 119.1 AIC: 317.2
##
##
## $results
## BIC crossover crossover 95%
## Vantage sensitivity STRONG "296.18" "-3" ""
## E only "296.73" NA ""
## Diathesis-stress STRONG "297.74" "3" ""
## G + E only "300.72" NA ""
## Differential susceptibility STRONG "300.75" "-3.64" "( -4.85 / -2.43 )"
## Vantage sensitivity WEAK "300.76" "-3" ""
## Diathesis-stress WEAK "301.01" "3" ""
## Differential susceptibility WEAK "305.29" "-10.45" "( -11.64 / -9.26 )"
## G only "308.3" NA ""
## Intercept only "322.48" NA ""
## Within observable range?
## Vantage sensitivity STRONG ""
## E only ""
## Diathesis-stress STRONG ""
## G + E only ""
## Differential susceptibility STRONG "No"
## Vantage sensitivity WEAK ""
## Diathesis-stress WEAK ""
## Differential susceptibility WEAK "No"
## G only ""
## Intercept only ""
## % of observations below crossover
## Vantage sensitivity STRONG "0"
## E only NA
## Diathesis-stress STRONG "1"
## G + E only NA
## Differential susceptibility STRONG "0"
## Vantage sensitivity WEAK "0.0714285714285714"
## Diathesis-stress WEAK "1"
## Differential susceptibility WEAK "0"
## G only NA
## Intercept only NA
##
## $E_range
## [1] -3 3
# fits[[1]] is the best model (based on BIC)
summary(GxE_test_BIC$fits[[1]])
## $fit_main
##
## Call:
## stats::glm(formula = formula, family = family, data = data, model = FALSE,
## y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.56675 -0.71752 -0.09223 0.78074 2.29434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.18623 0.24026 9.099 1.28e-14 ***
## G:E 0.03148 0.01006 3.128 0.00233 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.066755)
##
## Null deviance: 112.85 on 97 degrees of freedom
## Residual deviance: 102.41 on 96 degrees of freedom
## AIC: 288.42
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_genes
##
## Call:
## stats::glm(formula = formula_b, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.56675 -0.71752 -0.09223 0.78074 2.29434
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## aes_T3 1.0000 0.1388 7.204 1.32e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.066755)
##
## Null deviance: 112.85 on 98 degrees of freedom
## Residual deviance: 102.41 on 96 degrees of freedom
## AIC: 288.42
##
## Number of Fisher Scoring iterations: 2
##
##
## $fit_env
##
## Call:
## stats::glm(formula = formula_c, family = family, data = data,
## model = FALSE, y = FALSE)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.56331 -0.71601 -0.09173 0.78066 2.29434
##
## Coefficients: (-1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## ev_T3 1.0000 0.3162 3.162 0.0021 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.066751)
##
## Null deviance: 112.85 on 98 degrees of freedom
## Residual deviance: 102.41 on 96 degrees of freedom
## AIC: 288.42
##
## Number of Fisher Scoring iterations: 2
plot(GxE_test_BIC$fits[[1]], xlim=c(-3,3), ylim=c(1,4), legend = "bottomright")